{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Data Analytics \n",
    "\n",
    "### Q-Q (Quantile-Quantile) and P-P (Probability-Probablity) Plots in Python \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analytics: QQ Plots\n",
    "\n",
    "Here's a demonstration of calculation of QQ-plots in Python. This demonstration is part of the resources that I include for my courses in Spatial / Subsurface Data Analytics and Geostatistics at the Cockrell School of Engineering and Jackson School of Goesciences at the University of Texas at Austin.  \n",
    "\n",
    "We will cover the following statistics:\n",
    "\n",
    "#### QQ-Plot\n",
    "* Convenient plot to compare distributions\n",
    "\n",
    "I have a lecture on QQ-plots available on [YouTube](https://www.youtube.com/watch?v=RETZus4XBNM).   \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  The dataset is available on my GitHub account in my GeoDataSets repository at:\n",
    "\n",
    "* Tabular data - [2D_MV_200wells.csv](https://github.com/GeostatsGuy/GeoDataSets/blob/master/2D_MV_200wells.csv)\n",
    "\n",
    "#### Importing Packages\n",
    "\n",
    "We will need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrays for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "from scipy import stats                   # inverse percentiles, percentileofscore function for P-P plots\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # plotting\n",
    "import matplotlib.gridspec as gridspec\n",
    "plt.rc('axes', axisbelow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Working Directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). Set this to your working directory, with the above mentioned data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"d:/PGE383\")                     # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make Data \n",
    "\n",
    "Let's specify two univariate Gaussian distribution and then sample from this distribution.\n",
    "\n",
    "* This allows us to vary the distributions and number of data and visualize the impact on the QQ-plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "n1 = 100; mean1 = 0.35; stdev1 = 0.06 \n",
    "n2 = 50; mean2 = 0.3; stdev2 = 0.05\n",
    "\n",
    "X1 = np.random.normal(loc=mean1,scale=stdev1,size=n1)\n",
    "X2 = np.random.normal(loc=mean2,scale=stdev2,size=n2)\n",
    "\n",
    "print()\n",
    "\n",
    "nq = 100\n",
    "xmin=0.0; xmax=0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the QQ-plot\n",
    "\n",
    "Calculate and match percentiles from both data distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumul_prob = np.linspace(1,99,nq)\n",
    "X1_percentiles = np.percentile(X1,cumul_prob)\n",
    "X2_percentiles = np.percentile(X2,cumul_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make the Q-Q Plot Visualization\n",
    "\n",
    "Let's look at the data histograms, cumulative distribution functions and QQ-plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "spec = fig.add_gridspec(2, 3)\n",
    "\n",
    "ax0 = fig.add_subplot(spec[:, 1:])\n",
    "plt.scatter(X1_percentiles,X2_percentiles,color='darkorange',edgecolor='black',s=10,label='Q-Q plot')\n",
    "plt.plot([0,1],[0,1],ls='--',color='red')\n",
    "plt.grid(); plt.xlim([xmin,xmax]); plt.ylim([xmin,xmax]); plt.xlabel(r'$X_1$ - Porosity (fraction)'); plt.ylabel(r'$X_2$ - Porosity (fraction)'); \n",
    "plt.title('Q-Q Plot'); plt.legend(loc='lower right')\n",
    "\n",
    "ax10 = fig.add_subplot(spec[0, 0])\n",
    "plt.hist(X1,bins=np.linspace(xmin,xmax,30),color='red',alpha=0.5,edgecolor='black',label=r'$X_1$',density=True)\n",
    "plt.hist(X2,bins=np.linspace(xmin,xmax,30),color='yellow',alpha=0.5,edgecolor='black',label=r'$X_2$',density=True)\n",
    "plt.grid(); plt.xlim([xmin,xmax]); plt.ylim([0,15]); plt.xlabel('Porosity (fraction)'); plt.ylabel('Density')\n",
    "plt.title('Histograms'); plt.legend(loc='upper right')\n",
    "\n",
    "ax11 = fig.add_subplot(spec[1, 0])\n",
    "plt.scatter(np.sort(X1),np.linspace(0,1,len(X1)),color='red',edgecolor='black',s=10,label=r'$X_1$')\n",
    "plt.scatter(np.sort(X2),np.linspace(0,1,len(X2)),color='yellow',edgecolor='black',s=10,label=r'$X_2$')\n",
    "plt.grid(); plt.xlim([xmin,xmax]); plt.ylim([0,1]); plt.xlabel('Porosity (fraction)'); plt.title('CDFs'); plt.legend(loc='lower right')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.4, wspace=0.3, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the P-P plot\n",
    "\n",
    "Calculate and match the values from both data distributions and plot the cumulative probilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_X = min(X1.min(),X2.min())\n",
    "max_X = max(X1.max(),X2.max())\n",
    "\n",
    "X_values = np.linspace(min_X,max_X,nq)\n",
    "\n",
    "X1_cumul_probs = []; X2_cumul_probs = []\n",
    "\n",
    "for X in X_values:\n",
    "    X1_cumul_probs.append(stats.percentileofscore(X1,X)/100)\n",
    "    X2_cumul_probs.append(stats.percentileofscore(X2,X)/100)\n",
    "    \n",
    "X1_cumul_probs = np.asarray(X1_cumul_probs); X2_cumul_probs = np.asarray(X2_cumul_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make the P-P Plot Visualization\n",
    "\n",
    "Let's look at the data histograms, cumulative distribution functions and QQ-plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vvl63Ps8RqdHvfgdvvgmPPgqnnBJ1NHWWsgTYzO4FTgC+cfeuyX1/B04E1gCfAee4+5JUxSDZZcmSBQwf3im08w0b9p9aHXf22WfTpUsXrr/+el5++WWeeeYZZsyYEVocIiK1UdGzt7B7GUUTxsHYCTUmtPV5jkitXHcdXHwxnHBC1JHUSyprgMcDx1XZ9xzQ1d33Az4F/i+F44uEIi8vj379+jFs2DAuueQSHnvsMVq2bBl1WCKSY+rTs1d9fiVU33wDd98d3D/kkIxNfiGFM8Du/qKZdaqyr/JP3qvAqakaXyRM5557Lvvssw9PPvkke+yxR9ThiEgOivXKD2ZxKaPk9TyKB9Xcs7c+zxGp1ldfQa9e8N//wrHHQseOUUfUIFHWAJ8L/DPC8UVq7dprr6Vdu3aUl5dv2Ddp0iSeeeYZvvnmGwoLC8nP1weLiKROfXr2qs+vhGLhQjjqqCAJnjIl45NfiCgBNrNhQDnw0BaOGQQMAmjXrh2JRCI9wVWyfPlyjRvhuG3atOHHH3/csL127VpWr14d2rjuvtH5N6e4uJjly5czfvx4rr/+eo455hgAevXqRa9evfjhhx+46qqr+NWvfrXJc1etWhXJeyoi2ak+PXvV51caZP78IPldvBhKS6Gaz7pMlPYE2MzOJrg4rpe7++aOc/cxwBiALl26eCwWS0+AlSQSCTRudON+9NFHtGrVasN2s2bNaB5ig20z2+j81Xn++ed5+OGHmTlzJq1atWLYsGF89tlndOvWbcMxw4cPZ/DgwdWeq0WLFhxwwAGhxSwiIpJWM2cGLc+mTg3qfrNEWhNgMzsO+BNwpLuXpXNsyXxt23Zk+PD5oZ2vdesOW3x8wYIFDBw4kGeeeWZDcnvppZcyatQoxo8fj7szdOhQevfuzYEHHhhaXCIiIpFbswa23hr69YPjjguWOc4iqWyDNgGIATua2ULgGoKuD82B5yxYLeRVd78wVTFIdhk8+NpQz1dT+UPHjh2ZO3fuRvsGDBjAgAEDgKA0YurUqSxdupQ5c+Zw4YX6VhbJdVV77qoHr2SkDz+E44+He+4Jyh+yLPmF1HaB6FfN7ntSNZ5Iul1yySVccsklUYchIo1E1Z67r50/mAfvHqUevJJZ3nsPjj4amjaFn/wk6mhSRivBiYiIhKByz10oY+KU+EbbiWmlSoClcXv7bTjmGMjLg+efhz33jDqilFECLCIiEoKqPXf7n19Ayd1zUQ9eyQhz5gTlDm3bwvTp0Llz1BGllBJgERGREFTXc/fQQw9VD17JDLvvDoWFcMEFWdHntyZKgKVRc3eSF0xmnC10+RORLFW156568Eqj9+KLQcLbqROMGBF1NGmzVdQBiGxOixYtWLx4cUYmku7O4sWLadGiRdShiIiIVK+0NFjWuKgo6kjSTjPA0mh16NCBhQsX8u2336bk/KtWrUppgtqiRQs6dNhyr2EREZFITJ4Mp5wCXbrAvfdGHU3aKQGWRqtZs2Z0TmERfiKR0CptIiKSeyZNgtNOg1/8IpgF3mGHqCNKO5VAiIiIiOSK9eth5Eg48ECYNi0nk1/QDLCIiIhIbnCHrbaCp58OFrpo3TrqiCKjGWARERGRbDduHJxwAqxaBdtvn9PJLygBFhEREcluY8bAuefC2rVBCYSoBCJXjBr1Z5YsWbDJ/rZtOzJ48LURRCQiIiIpd/vtQZuzPn3g8cdB7TkBJcA5Y8mSBQwf3mmT/cOHz097LCIiIpIGd9wRJL8nnQT//Cc0bx51RI2GSiBEREREslGPHnD++TBxopLfKpQAi4iIiGQL96C9mTvsv39Q/9usWdRRNTopS4DN7F4z+8bMPqi0b3sze87M/pP8ul2qxhcRERHJKe5w5ZVw9NEQj0cdTaOWyhng8cBxVfYNBaa5+57AtOS2iIiIiDSEO1x+OdxwA1xwAZx4YtQRNWopS4Dd/UXg+yq7TwLuS96/Dzg5VeOLiIiI5IT16+GSS+Dmm4OL3u68M1jwQjYr3V0gdnb3rwDc/Ssz22lzB5rZIGAQQLt27UgkEumJsJLly5dnzbiLFi1i/vzq91eMlU2vtzGPKyIiEqo33oCSkmAGeORIMIs6okav0bZBc/cxwBiALl26eCwWS3sMiUSCbBk3kRhPp06dNtnfvj0bxsqm19uYxxUREQnVoYfCm2/CAQco+a2ldM+Pf21mPwFIfv0mzeOLiIiIZL7ycjjnHHjmmWD7wAOV/NZBuhPgOHB28v7ZwJNpHl9EREQks61dC/36wfjx8OGHUUeTkVLZBm0CMBPoYmYLzew84AbgGDP7D3BMcltERKTRicfjXHbpxcTVTkoak9WroW9feOyx4KK3K66IOqKMlLIaYHfvt5mHeqVqTBERkTDE43GKBvajsHsZRRPGwdgJFBQURB2W5Lo1a+CUU2DyZLj9digsjDqijKUeGSIiIlUkppVS2L2MIT2hsHsZiWmlUYckEqzo1qlTsLqbkt8GabRdIERERKIS65UfzPxSRsnreRQPyo86JMlly5fDt99C585BuzNpMCXAIiIiVRQUFMDYCSSmlVI8KF/lDxKdZcugTx/44gv46CNo0SLqiLKCEmAREZFqFBQUKPGVaP3wAxx3HLz9NkyYoOQ3REqARURERBqbxYshPx/efz/o+HDSSVFHlFWUAIuIiIg0NkOHwuzZ8OST0Lt31NFkHXWBEBEREWlsbroJpk1T8psitUqAzexxMzvezJQwi4iIiKTCF1/AeedBWRm0aQM9ekQdUdaqbUJ7J/B74D9mdoOZ7Z3CmERERERyy3//C0ccARMnwqefRh1N1qtVAuzuU939DOBAYD7wnJnNMLNzzKxZKgMUERERyWpz5wbJ7/ffw9Sp0K1b1BFlvVqXNJjZDsAAYCDwDnArQUL8XEoiExEREcl2//lPkPwuXx7U/HbvHnVEOaFWXSDM7F/A3sADwInu/lXyoX+a2ZupCk5EREQkq5WXw3bbweTJsN9+UUeTM2rbBm2su0+uvMPMmrv7anc/OAVxiYiIiGSvL76AXXaBffaBd9+FrdRnIJ1q+27/tZp9M8MMRETSx8yOM7NPzGyOmQ2t5vE2ZvaUmb1rZrPN7Jwo4hQRyUpvvx3M9t54Y7Ct5DfttviOm1l7MzsIaGlmB5jZgclbDMir76Bm9sfkh+oHZjbBzLS2n0iamFkToAToDewL9DOzfascVgh86O77AzHgH2a2dVoDlZwWj8e57NKLicfj1W6HcU6RKLT66CPo1Qu23Rb69o06nJxVUwnEsQQXvnUAbq60/0fgyvoMaGa7ApcA+7r7SjN7FDgdGF+f80njMOrPf2bJggXVPta2Y0cGX3ttmiOSLegOzHH3uQBm9ghwEvBhpWMcaGVmBmwLfA+UpztQyU3xeJyigf0o7F5G0YRxvHb+YB68e9SGbcZOoKCgoEHnrM85RBpsxgz2v/xyaN8enn8efvrTqCPKWVtMgN39PuA+M/utuz8e8rgtzWwtwUzylyGeWyKwZMEChnfqVO1jw+fPT2ssUqNdgc8rbS8EDq1yzO1AnOBnsxXwO3dfX/VEZjYIGATQrl07EolEKuLNOcuXL8/p9/Kh+8dR2L2MIT0Byhg/ccJG2w/dP47WrVvX6lwV72XVc9blHKLvyTA0Xb6cQ/v1Y3Xbtrx/ww2snjcP5s2LOqyctcUE2Mz6u/uDQCczu6zq4+5+czVP2yJ3/8LMbgIWACuBUncvrWbsyD9Yo/qBT8W4ixYtoro8dNGiRRvGasi4ixYtoprTbzJGdbLpfc4QVs0+r7J9LDALOArYg6D390vuvmyjJ7mPAcYAdOnSxWOxWOjB5qJEIkEuv5fLli2jaGApUEbJ63n0P78fJXeP2rBdPPacWr8/Fe9l1XPW5Ryi78nQPPwwb6xZw69/+9uoI8l5NZVAbJP8um1YA5rZdgR/bu0MLAEmVkq0N2gMH6xR/cCnYtxEYjydqpmhbd+eDWM1ZNzE+OrPD9AetnjebHqfM8RCYLdK2x3Y9K8w5wA3uLsDc8xsHkErxNfTE6LksoKCAhg7gcS0UooH5VNQUMChhx660XYY5xRJiylTYOVKOOUUOPFE1uTmxEujU1MJxF3Jr38JccyjgXnu/i1s6DH8a+DBLT5LRMLyBrCnmXUGviCowf99lWMWAL2Al8xsZ6ALMDetUUpOKygo2ChJrbodxjlFUi4eDy50O+AAOPlkdXtoRGr1L2FmI82stZk1M7NpZvadmfWv55gLgF+aWV7yAptewEf1PJeI1JG7lwMXA88S/Ow96u6zzexCM7swedh1wK/N7H1gGvAnd/8umohFRDLQY4/Bb38bLGs8ZYqS30amtgth5Lv7EDP7DcGfT/sC06nHrK27v2ZmjwFvE1xV/g7JUgdJv3fffYfhwwcAFbW64wFo27Yjgwerc0O2Si5sM7nKvtGV7n8J5Kc7LhGRrPDww3DWWXDooUHyqwsuG53aJsDNkl/7ABPc/ftg8rZ+3P0a4Jp6n0BC476C4cM7ATB/PhvqeIcPnx9VSCLSyMXjcRLTSon1qr6WtrrHhw0bRumUOPm9CxgxYkS6QxZJr3fegR494Jlngn6/0ujUNgF+ysw+JujacJGZtQNWpS4sERFpjGrqp1vd46+99hp333o9l8fgpls/AFASLNlp2bJgtnfkSFi9Glpona/GqlYFKe4+FPgVcLC7rwVWEHRyEBGRHJKYVrqhn25h9zIS00prfLx0SpzLYzCkJ1weg9IpWolNslBJCey9d/DnVDMlv41cbWeAAfYh6Adc+Tn3hxyPiIg0YrFe+cHMbkU/3UH5NT7ects2G2Z+b0rA+ZeqE4NkmVtugcsug4IC+MlPoo5GaqFWCbCZPUDQDH8WsC6521ECLCKSU2rqp1vd4xXHTJwS5/xLVQMsWeaGG+D//i/o+PDww7D11lFHJLVQ2xngg4F9k03xRUQkh9XUT7e6x0eMGKHEV7LP/fcHyW+/fsH9pnX5w7pEqbb/Uh8QLOj1VQpjEREREckcp5wCX34JV1wBTZpEHY3UQW0T4B2BD83sdWB1xU53VyGXiIiI5A53uPNOOPNMaNUKhg6NOiKph9omwMNTGYSIiKRPTX18RWQz3GHwYLjtNigvh0suiToiqafatkF7AZgPNEvef4NgJTcREckgFX16239WQtHAfsTjakkmUivr18Mf/hAkv3/8IxQVRR2RNEBtu0CcDwwCtifoBrErMBrolbrQJEpTn53CrJd/tsn+ltvuwoRJL0YQkYiEoXKfXgj69GoWWKQG69bB+efDuHFBycP11we9fiVj1bYEohDoDrwG4O7/MbOdUhaVRG4ryph01T6b7D/5rwsjiEZEwlJTH18RqcbXX8Ozz8I11wQ3Jb8Zr7YJ8Gp3X2PJf/DkYhhqiSYikmFq6uMrIpWUlwfdHXbZBd57D3bYIeqIJCS1TYBfMLMrgZZmdgxwEfBU6sISEZFUqamPr4gAa9bA6adDx44wapSS3yxTq4vggKHAt8D7wAXAZOCqVAUlIiIiEplVq4Iev088AXvsEXU0kgK1mgF29/VmNgmY5O7fNnRQM2sLjAW6EpRSnOvuMxt6Xmmc3n3nHYYPGLDJ/rYdOzL42mvTH5CIiMjmlJXBb34DpaVw110waFDUEUkKbDEBtqDo9xrgYsCSu9YBxe7ekMzlVuDf7n6qmW0N5DXgXNLI+YoVDO/UaZP9w+fPT3ssIo1dPB7nofvHsWzZsmrLFNTDVySF3OG3v4XnnoN774Vzzok6IkmRmkogBgM9gEPcfQd33x44FOhhZn+sz4Bm1ho4ArgHwN3XuPuS+pxLRCSbVPToPWjVpGp79KqHr0iKmcGFF8IDDyj5zXI1JcBnAf3cfV7FDnefC/RPPlYfuxPUE48zs3fMbKyZbVPPc4mIZI3KPXoLuwc9euvyuIjU05IlMGVKcP+kk+CMMyINR1KvphrgZu7+XdWd7v6tmTVrwJgHAkXu/pqZ3Upwkd3VlQ8ys0EEi2/Qrl07EolEPYerv+XLl2fNuIsWLaK6ioOVK1cyP/nAmjVrNtwvX7eOJUuWVHt8dbEtWrSIak6/yRhVn5NIJLLqfRZpiJp69KqHr0gKfP895OfDhx/C3LnQvn3UEUka1JQAr6nnY1uyEFjo7q8ltx8jSIA34u5jgDEAXbp08VgsVs/h6i+RSJAt4yYS4+lUTR1uy5YtN+yfP3/+hvtNmzShbdu21Ry/vNrYEuOrP3/VMSprD8Risax6n0UaoqJH70P3j6N47Dmb1Piqh69IyL79Fo4+Gj75BB5/XMlvDqkpAd7fzJZVs9+AFvUZ0N0XmdnnZtbF3T8hWE75w/qcSxqPd//7DsOXzKr2sa99cXqDEclgBQUFtG7derP/OVMPX5GQLFoEvXoFs77xeDALLDljiwmwuzdJ0bhFwEPJDhBzAVWaZzhvsoLhgztU+9jzRZ+lORoREZEa/POfMH8+TJ4MPXtGHY2kWW1XgguVu88CDo5ibBEREclh7kG3h0sugYIC6Nw56ogkArVdCU5EREQks82bB4ceCrNnB0mwkt+cFckMsIiIiEha/ec/cNRRsGJFsNSx5DQlwCIiIpLdPvoouOBt7VqYPh323z/qiCRiSoBFREQke/3nPxCLBSUPiQT8/OdRRySNgGqARUREJHt16BC0OHvhBSW/soFmgEVERCT7vPsu/PSn0LYtPPBA1NFII6MZYBEREckuM2fCEUfAhRdGHYk0UkqARUREJHu8+GJQ8rDTTvD3v0cdjTRSKoGQOvl+8WKGDxiwyf4lixcD1a8EJyIikhbTpgWLW3TsGNzfZZeoI5JGSgmw1E15OcM7ddpk9xFvlac/FpEsEI/HSUwrJdYrn4KCgqjDEclc5eVw8cWw++4wdSrsvHPUEUkjphIIEZGIxONxigb2o/1nJRQN7Ec8Ho86JJHM1bQpTJ4c9PlV8is1UAIskqHMbBszaxJ1HFJ/iWmlFHYvY0hPKOxeRmJaadQhiWSeJ54IZn7dg6WNd9wx6ogkAygBFskQZraVmf3ezJ4xs2+Aj4GvzGy2mf3dzPaMOkapm1ivfEpez2PkdCh5PY9Yr/yoQxLJLP/8J/TtC2+/DWVlUUcjGUQ1wCKZYzowFfg/4AN3Xw9gZtsDPYEbzOwJd38wwhilDgoKCmDsBBLTSikepBpgkTp54AEYMAAOOwyefhq22SbqiCSDKAGWyLz7zjsMHzCARYsWkRg/fsP+T+bOpcvuu29yfNuOHRl87bVpjLDROdrd11bd6e7fA48Dj5tZs/SHJQ1RUFCgxFekrsaNg/POg549IR5X8it1FlkCnKxdfBP4wt1PiCoOiY6vWMHwTp2YD3Sq1Fni5JdfZvhRR21y/PD589MVWqNUkfyaWXPgt0AnKv0Mu/u11SXIIiJZ5yc/CdqdTZgALVtGHY1koChrgC8FPopwfJFM9SRwElAOrKh0ExHJbh8l04bjjoNJk5T8Sr1FMgNsZh2A44ERwGVRxCCSwTq4+3FRByEiklZ//zsMHRr0+O3ZM+poJMNFVQIxChgCtNrcAWY2CBgE0K5dOxKJRFoCq2z58uVZM+6iRYuoroJg5cqVzE8+sGbNmg33y9etY8mSJZscX75u3YZjqu6v7vgtPadi7MrjVo2p6msI832J6t83BDPM7Bfu/n7UgYiIpMVf/wpXXw2nnx5c9CbSQGlPgM3sBOAbd3/LzGKbO87dxwBjALp06eKx2GYPTZlEIkG2jJtIjN+ozrZCy5YtN+yfP3/+hvtNmzShbdu2mxzftEmTas/T9K3qj9/ScyrGrjxu1Zgqaw+hvi9R/fuG4DBggJnNA1YDBri77xdtWCIiIXOHa66B666DM88MLn5rovbn0nBRzAD3AArMrA/QAmhtZg+6e/8IYslIo0b9mSVLFmyy/5NP5tKly6bdEwA+/PAdgmumNrZk8WISkyYBwYzo/FmzAFizenVI0UoK9I46ABGRtEgkguT3vPPgrruU/Epo0p4Au/v/EfQxJTkDfLmS37pZsmQBw4d32mT/ySe/zPDhm3ZPqHisOuvLy4klZ26XwIZZXF+/vuGBSkq4+3/NbH/g8OSul9z93ShjEhFJiZ494amnoE8f2Eprd0l49N0kkmHM7FLgIWCn5O1BMyuKNioRkZCsXw9/+hO8806wfcIJSn4ldJEuhOHuCSARZQwiGeg84FB3XwFgZjcCM4HiSKMSEWmo9evhggtg7NhgcYsDDog6IslSWglOJPMYsK7S9rrkPhGRzLVuHZx7Ltx/f9Dx4eqro45IspgSYJHMMw54zcyeSG6fDNwTXTiyOfF4nMS0UmK98rXcsciWrF0LZ50FjzwSXPR21VVRRyRZTkU1IhnG3W8GzgW+B34AznH3UZEGJZuIx+MUDexH+89KKBrYj3g8HnVIIo3X+vWwbBmMHKnkV9JCM8AiGcjd3wLeijoO2bzEtFIKu5cxpCdAGYlppZoFFqlq9WpYsQK23x7icbU5k7TRDLBIhjCzl5NffzSzZZVuP5rZsqjjk43FeuVT8noeI6dDyet5xHrlRx2SSOOyciWcdBLk5wclEEp+JY00AyySIdz9sOTXzS4hLo1HQUEBjJ1AYlopxYNUAyyykRUroKAApk+Hu++GZs2ijkhyjBJgkQxjZje6+59q2ifRKygoUOIrUtWPP8Lxx8Mrr8B99wVLHIukmRLgRmxzSx5vblnjdFi2djXDZ03aZP9KUr908rvvvMPwAQOqfaxtx44MvvbalMfQSBwDVE12e1ezT0Sk8fnDH2DGDHj4Yfjd76KORnKUEuBGbEtLHkeleYv1DB/cdpP9UwanfulkX7GC4Z06VfvY8PnzUz5+1MzsD8BFwB5m9l6lh1oBM6KJSkSkjq6/Hk4/PVjhTSQiSoBFMsfDwBTgb8DQSvt/dPfvowlJRKQWvvsOSkqCFmcdOwY3kQipC4RIhnD3pe4+H1gDLHX3/7r7fwE3s3ujjU5EZDO+/hp69oQbboDZs6OORgRQAiySifZz9yUVG+7+A3BAdOGIiGzGl19CLAaffQZPPw377Rd1RCKAEmCRTLSVmW1XsWFm26NyJhFpbD7/HI48EhYuhH//G3r1ijoikQ30oZkjlixeTGLSpE32r1md+u4NErp/ADPM7LHkdl9gRITxiIhsat48WL4cSkvhV7+KOhqRjaQ9ATaz3YD7gfbAemCMu9+a7jhyzfrycmJt226y39envnuDhMvd7zezN4GjAANOcfcPIw5LRCSwfDlsuy0ccQTMnQstW0YdkcgmoiiBKAf+n7vvA/wSKDSzfSOIQySTfQW8DrwL7GhmR0Qcj4gIfPIJ7LNPsMAFKPmVRivtM8Du/hXBhzfu/qOZfQTsCmgGS6QWzGwgcCnQAZhF8B/JmQQzwiIi0Zg9O6jzdYcDD4w6GpEtivQiODPrRHD1+mtRxiGSYS4FDgH+6+49CX6Gvo02JKlOPB7nsksvJh6PRx2KSGq9+27Q7WGrrSCRgF/8IuqIRLYosovgzGxb4HFgsLsvq+bxQcAggHbt2pFIJNIbILB8+fJIx120aBHVLXC2cuVK5lfzwOb2A5SvW8eSJUs22b/efcP+dZWOqbx/c8fXZn/F2NXF9cXar7n85QdZt24dTRa+vNH+ur6+RYsW1fnfKqp/3xCscvdVZoaZNXf3j82sS9RBycbi8ThFA/tR2L2MognjYOwECgoKog5LJHzffgtHHQV5efD887DnnlFHJFKjSBJgM2tGkPw+5O7/qu4Ydx8DjAHo0qWLx2Kx9AWYlEgkiHLcRGI8napZ+rdly5Z12g/QtEkT2lZzEdxWZhv2L1myZMP9yvs3d3xt9leMXW28reGmqzpsNC7AEUVf1/n1tYc6/1tF9e8bgoVm1haYBDxnZj8AX0YakWwiMa2Uwu5lDOkJUEZiWqkSYMlO7drBdddB797QuXPU0YjUShRdIAy4B/jI3W9O9/gimc7df5O8O9zMpgNtgH9HGJJUI9YrP5j5pYyS1/MoHpQfdUgi4XrlFWjaFA49FC66KOpoROokihngHsCZwPtmNiu570p3nxxBLCIZJfkfyA7u/jmAu78QcUiyGQUFBTB2AolppRQPytfsr2SXRAJOOAG6doWZM8Es6ohE6iSKLhAvE/QuFZE6cnc3s0nAQVHHIjUrKChQ4ivZ57nn4KSTgnKHSZOU/EpG0lLIIpnnVTM7pCEnMLPjzOwTM5tjZkM3c0zMzGaZ2Wwz00yziMDkyXDiibDXXsEscPv2UUckUi9aClmywrvvvMPwAQM22d+2Y0cGX3tt+gNKrZ7ABWb2X2AFwV9U3N33q82TzawJUAIcAywE3jCzeOXV5JIX2d0BHOfuC8xsp5Bfg4hkovvvD8oeSkth++2jjkak3pQAS1bwFSsYXk2HiOGbaZuWiczsAXc/k6A7yhMNOFV3YI67z02e9xHgJDZejOb3wL/cfQGAu3/TgPFEJMPZunXBnfvvh5UroU2baAMSaSCVQIhkjoPM7KfAOcAy4Mcqt9raFfi80vbC5L7K9gK2M7OEmb1lZmfVP2wRyWgPP8xBF1wA330HW2+t5FeygmaARTLHaIJ2Z7sDb7HxxaSe3F8b1V2x4lW2mxJcaNcLaAnMNLNX3f3TjU7UCBasyUYZvEhLo6P3smHa//vfdBk5klW/+AXvvPEG61q2jDqkjKfvycZBCbBIhnD324DbzOxOd/9DA061ENit0nYHNl1IYyHwnbuvAFaY2YvA/sBGCXBjWLAmG2XwIi2Njt7LBhgzBm68EY45hg8vu4wjjjsu6oiygr4nGweVQIhkiGQPYLaU/FYcU4M3gD3NrLOZbQ2cDsSrHPMkcLiZNTWzPOBQ4KP6RS4iGeeBB+CCC+D44yEeZ32LFlFHJBIqzQCn0ahRf2bJkgWb7P/kk7l06fK/v14vWrSIRGI8H374DtBpk+OXLF5MYtKkavdLVptuZo8DT1ZcnAaQTGIPA84GpgPjt3QSdy83s4uBZ4EmwL3uPtvMLkw+PtrdPzKzfwPvAeuBse7+QSpelIg0QkcfDZdcAn//e1D3K5JllACn0ZIlCxg+vNMm+08++WWGDz9qw/b8+dCpUydOPvnlas+zvrycWNu21ez/LKRIpZE6DjgXmGBmnYElQAuCJLYUuMXdZ9XmRMmVFydX2Te6yvbfgb83OGoRyRxPPBH0+f3JT+DWW6OORiRlVAIhkiHcfZW73+HuPYCfElygdqC7/9Tdz69t8isisgl3+Mtf4JRT4J57oo5GJOWUAItkIHdf6+5fufuSqGOR/4nH41x26cXE41VLqkUaMXcYNgyGD4cBA2DgwKgjEkk5JcAiIiGIx+MUDexH+89KKBrYT0mwZAZ3uPxy+Nvfgove7rkHmjSJOiqRlFMNcCNQ9aK25cuXM3/WLBYtXFjtxW5rVq9OX3AZbnNLJH8ydy5t8vJIjB+/0f4sXTpZ0iAxrZTC7mUM6QlQRmJaKQUFBVGHJbJlc+fCXXdBUVFQ81urRjIimU8JcCNQ9aK2JUDbtm3xzVzs5uvXpyu0jLe5JZJPfvll/nbYYXSq8lgmLJ2cbHV2BrC7u19rZh2B9u7+esSh5bRYr3yKJowDyih5PY/iQflRhySyee5BsrvHHjBrVvBVya/kEJVAiGSeO4BfAf2S2z8CJdGFIwAFBQUUj53Aoj0KKR47QbO/0nitWwfnngu33x5s/+xnSn4l50QyA2xmxwG3ErRvGuvuN0QRh0iGOtTdDzSzdwDc/YdkL2CJWEFBgRJfadzKy+Hss+Hhh6Fz56ijEYlM2meAzawJwWxVb2BfoJ+Z7ZvuOEQy2Nrkz5EDmFk7gsUqREQ2b+1a6NcvSH7/9jf485+jjkgkMlGUQHQH5rj7XHdfAzwCnBRBHCKZ6jbgCWAnMxsBvAxcH21IItKorV8PffvCY4/BzTfD0KFRRyQSKXP39A5odipwnLsPTG6fSfAn3YurHDcIGJTc7ApEsQzrjsB3GlfjhqyLu7dqyAnMbG+ChTAMmObuH4USWQN06dLFP/nkk6jDyAqJRIJYLBZ1GFlB72Ult94KTZtCYWGdn6r3MTx6L8NjZm+5+8H1eW4UNcDVVdpvkoW7+xhgDICZvVnfF9gQGlfjpmrchp7D3T8GPg4hHBHJZmVl8MkncMABcOmlUUcj0mhEkQAvBHartN0B+DKCOEQyipn9SDX/WST4T6W7e+s0hyQijdny5XDiifDOO0G/3+23jzoikUYjigT4DWBPM+sMfAGcDvw+gjhEMkpDyyZEJIcsWwZ9+sCrr8L99yv5Faki7Qmwu5eb2cXAswRt0O5199k1PG1M6iPTuBo368cVkVzwww9w3HHw9tvwyCNw6qlRRyTS6ETSB9jdJwOT63B8JAmDxtW4jWlcM3vZ3Q+rVAphlb+qBEJEABg1Kih7eOwxOElNlkSqo6WQRTKEux+W/KpSCBHZvKuvhhNOgEMOiToSkUZLSyGLZBgzu7E2+0Qkh3z1FZx8cvC1aVMlvyI1aFQJsJkdZ2afmNkcM9ukS7cFbks+/p6ZHZimcfc2s5lmttrMLg9jzFqOe0bydb5nZjPMbP80jXtScsxZZvammR2WjnErHXeIma1L9oxO+bhmFjOzpcnXO8vMQlkeqTavNzn2LDObbWYv1PLUx1Szr3f9I81N8Xicyy69mHg83qBjRCK3cCEceSRMnRp0exCRmrl7o7gRXBD3GbA7sDXwLrBvlWP6AFMIah5/CbyWpnF3Ag4BRgCXp/H1/hrYLnm/dxpf77b8b5GU/YCP0zFupeOeJ6gRPzVNrzcGPB3B93Nb4EOgY8X3WQ3n/APwPrACeK/SbR7wUJjx1+e21157eaZ48sknvWO7PL/xeLxjuzx/8skn63VMqkyfPj1tY2W7rH8v5893331391at3F9+OWXDZP37mEZ6L8MDvOn1/MxqTDPAtVki+STg/uTrfhVoa2Y/SfW47v6Nu78BrG3gWHUdd4a7/5DcfJWgZ3I6xl2e/MYC2Ibqe8+GPm5SEfA48E0IY9Zl3LDVZtzfA/9y9wUQfJ/VcM6HgROBePJrxe0gdz8jzOCzXWJaKYXdyxjSEwq7l5GYVlqvY0QiNW8eHHEELF4Mzz0HPXpEHZFIxmhMCfCuwOeVthcm99X1mFSMmwp1Hfc8gtnvtIxrZr8xs4+BZ4Bz0zGume0K/AYYHcJ4tR436Vdm9q6ZTTGzn6dp3L2A7cwsYWZvmdlZWzqhuy919/nAOUAP4AzgbODisMo2ckWsVz4lr+cxcjqUvJ5HrFd+vY4RidQ228BPfwrPPw+HHhp1NCIZpTF1gajNEsm1WkY5BeOmQq3HNbOeBAlwGLW4tV2K+gngCTM7ArgOODoN444C/uTu68yqOzxl474N/NTdl5tZH2ASsGcaxm0KHAT0AloCM83sVXf/tIZzTwKWAm8BqxsYZ04qKCiAsRNITCuleFB+sF2PY0QiMXcudOgAO+0EL7wA4f2+FMkZjSkBrs0SyalYRjmqpZlrNa6Z7QeMBXq7++J0jVvB3V80sz3MbEd3/y7F4x4MPJJMfncE+phZubtPSuW47r6s0v3JZnZHml7vQuA7d18BrDCzF4H9gZoS4A7uflwDYhOCBLempLY2x4ik1XvvwdFHw29/C3feqeRXpJ4aUwnEhiWSzWxrgiWSq156HQfOSnaD+CWw1N2/SsO4qVDjuGbWEfgXcGYtZgXDHPdnlsxCk502tgYamnzXOK67d3b3Tu7eCXgMuKiByW+txjWz9pVeb3eCn4uUv17gSeBwM2tqZnnAocBHtTj3DDP7RQPjE5FM8/bb0LMnbL01/PGPUUcjktEazQywb2aJZDO7MPn4aILOAH2AOUAZQS1kysc1s/bAm0BrYL2ZDSa4on/Z5s4bxrjAn4EdgDuS+Vm5ux9c3zHrMO5vCf6jsRZYCfyu0kVxqRw3dLUc91TgD2ZWTvB6T0/H63X3j8zs3wSdHNYDY939g1qc/jDgHDObS1ACUbES3H4NiVlEGrHXX4djj4XWrYOa3z32iDoikYxmDfycF5E0M7OfVrff3f+b7lgq69Kli3/yySdRhpA1EokEsVgs6jCyQla8l6tXw557QrNmQfL702p/BaRUVryPjYTey/CY2Vv1nRhsTCUQIlI7C4DDgbOTSa8DO0cbkoikTPPm8NhjwQVvESS/ItlICbBI5rkD+BXQL7n9I1ASXTgikhJTp8IttwT3u3cPOj+ISCiUAItknkPdvRBYBZBcLGXraEMSkVBNmQInnADjxsGqVVFHI5J1lACLZJ61ZtaEZF9hM2tHcBGdiGSDeBxOPhn23RemT4cWLaKOSCTrKAEWyTy3AU8AO5nZCOBl4PpoQxKRUDz+eNDjt1s3mDYNdtgh6ohEspIS4BQzs3VmNsvMPjCzicl+r6kY52Azuy15P2Zmv67HOQZXLMdrZnsn437HzBrUb8fMuiVXWKvYLjCzofU8V7tk67Cc5e4PAUOAvwFfASe7+8RooxKRUCxeHCxr/NxzsN12UUcjkrWUAKfeSnfv5u5dgTXAhbV5kpnVqUezu7/p7pckN2NAnRLg5HjnAg8nd50MPOnuB7j7Z5WOMzOr6/dNN4L+zRWxxt39hjqeo+K53wJfmVmP+jw/W7j7x+5e4u63u3ttFs8Qkcbsm2+Cr4MGQSIR9PsVkZRRApxeLwE/M7PtzWySmb1nZq8mlzvGzIab2RgzKwXuN7Ofmtm05HHTkivDYWZ9kzPK7yaXz62Y9X3azDoRJNl/TM7gHm5m88ysWfK41mY2v2K7kqOAt5MLOPQBBgMDzWy6mXUys4/M7A7gbWA3M7vTzN40s9lm9peKk5jZIWY2Ixnb62bWBrgW+F0ynt+Z2QAzuz15/OZe43gzuy15rrlmdmqlWCcBZ4T475JRzOw+M2tbaXs7M7s3wpAywrBhwzjkwF8wbNiwqEMR2djYsbD77vDmm8F200azRpVI1lICnCbJGdbewPvAX4B3kit3XQncX+nQg4CT3P33wO3A/cnjHiKo/YRghbhj3X1/oKDyOO4+HxgN3JKceX4JSADHJw85HXjc3ddWCbEH8FbyHJMrnaNn8vEuyVgOSPaeHZZsPr0fcKSZ7Zdc8vefwKXJ2I4GViTj/Wcynn9WGXdzrxHgJwSrnp0AVJ4xfpOgD26u2s/dl1RsJLtAHBBdOI3fsGHDuPvW6+m7ywfcfev1SoKl8SgpgfPPhyOOgK5do45GJGcoAU69lmY2iyBpWwDcQ5DUPQDg7s8DOyRnSgHi7r4yef9X/K8k4YHk8wBeAcab2fkEy+zWZCz/Wzb6HGBcNcf8BPh2C+f4r7u/Wmn7NDN7G3gH+DmwL0GS/JW7v5F8bcvcvbyG2Db3GgEmuft6d/+QjRd6+AbYpYbzZrOtzGxDcaCZbU8jWta8MSqdEufyGAzpCZfHgm2RyN1yC1x8MRQUwBNPqNuDSBrpQzP1Vrp7t8o7zMyqOa5iTeoVWziXA7j7hWZ2KMGs7iwz67aF5+DuryTLGI4Emrj7B9XFCWzpt++GuMysM3A5cIi7/2Bm45PPtUqvo74qP391pfuV37MWyXhz1T+AGWb2GMH7dRowItqQGrf83gXcdGvwbX9TAs6/tGDLTxBJtcmT4bLLgo4PDz8MW6uVt0g6aQY4Gi+SrGE1sxjwnbsvq+a4GQQlCySPfzn5nD3c/TV3/zPwHbBblef9CLSqsu9+YALVz/4CfAT8rJbxtyZIiJea2c4EpR0AHwO7mNkhyThbJUs/qounQrWvsQZ7AdUl8Vkv+Z+n6cBvga8JZu1PcfcHIg2skRsxYgTnX3olE7/syvmXXsmIEfr/gkTs2GPhzjvhkUeU/IpEQAlwNIYDB5vZewS1rWdv5rhLgHOSx50JXJrc/3cze9/MPiBIpt+t8ryngN9UXASX3PcQsB1BElydKcARtQne3d8lKH2YDdxLUJKBu68BfgcUm9m7wHMEs7XTgX0rLoKr5Wvckp7AM7WJNdu4uxOUhnyY7ABRnCwRkRqMGDGCN95+X8mvRMcd/vEP+PxzaNIELrxQF7yJREQ/eSnm7ttWs+974KRq9g+vsj2foDtD1eNOqWaoRPKGu39KcHFaZYcBj1W+eKrKOf9rZovNbE93/0/lWJJxdK1y/IDNnOcN4JfVPHRIle3xlc5d3WscUGW78vtYQDXvXw551cwOqai1FpEM4A5DhsBNN8GyZfCXv9T8HBFJGSXAOcDMignKFPrUcOhQgovh/pPyoOrJgmV/b052PshVPYELzWw+QSmKEUwOV/1Pj4g0Bu4weDDcdhsUFsI110QdkUjOUwKcA9y9qJbHfQJ8kuJwGiS5EMakqOOIWO+aDxGRRmH9erjoIrjrLvjjH4MSiGqvgxaRdFINsEjmWUDQB/nsZE9mZ+M2cSLSWCxfDq+9BkOHKvkVaUQ0AyySee4A1hPUTl9L0GXjcTatsxaRqJSXw7p1wZLGL78MeXlKfkUaESXAIpnnUHc/0MzegWAluOQqfCLSGKxdC2ecAWVl8OSTsM02UUckIlWoBEIk86w1syYkFw1JXhi4PtqQRASANWvgtNNg4kTo2TNodyYijY4SYJHMcxvwBLCTmY0gWDzk+mhDEhFWrYJTToFJk4KOD//v/0UdkYhshhLgLGVmvzezN81suZl9ZWZTzOwwMxtuZmvN7Mfk7VMzu93MflLpuTEzW598bsXtqShfj/yPuz8EDAH+BnwFnOzuE6ONSkQ45xx45hkYPRqKatV8R0QiohrgLGRmlxH09L0QeBZYAxxHsHjECuCf7t7fzJoRLCv8F+AtMzvI3b9KnuZLd++Q/uhlc8ysBcG/6c+A94G73L082qhEZIPLL4feveGss6KORERqoBngLGNmbQg6AxS6+7/cfYW7r3X3p9z9isrHJvfPJli++Fugxr/XmVn35MzyMjP72sxuTskLkercBxxMkPz2Bm6KNhwRYdkyGD8+uH/QQUp+RTKEZoCzz6+AFgQ1orXi7uvM7Eng2Focfitwq7s/YGbbUmWJZEmpfd39FwBmdg/wesTxiOS2JUvguOPgrbfgl7+EvfeOOiIRqSXNAGefHYDv6vGn8S+B7Stt72JmSyrdTkvuXwv8zMx2dPfl7v5qGEFLraytuKPSB5GIff89HH00vP120PFBya9IRtEMcPZZDOxoZk3rmCTtCnxfaXtzNcDnEZRYfGxm84C/uPvT9Q9X6mB/M1uWvG9Ay+S2Ae7uraMLTSSHfPstHHMMfPwxPPEEHH981BGJSB0pAc4+M4FVwMnAY7V5gpltBZwITK3pWHf/D9Av+ZxTgMfMbAd3X1HviKVW3F0NRUUag5degjlzIB6H/PyooxGRelACnGXcfamZ/RkoMbNyoJTgT+dHAz2Bsopjk10gfgYMB9oDNV7QZmb9gWfd/VszW5LcvS7M1yAi0iitWxcsbHHKKdCjB+y8c9QRiUg9qQY4C7n7zcBlwFUE3R0+By4GJiUP+Z2ZLQeWAHGCsomD3P3LWpz+OGB28vm3Aqe7+6pQX4CISGOzYAHsvz+UlgbbSn5FMppmgLNUcrGEh6p5aAbBjO+WnpsAqu0B7O79GxqbiEhGmTcPjjoKfvgBWqvUXiQbaAZYJMOY2cVmtl3UcaRbPB7nsksvJh6P1+txkXqZMweOOAKWLoVp04J2ZyKS8UJNgM3sXjP7xsw+2MzjZma3mdkcM3vPzA4Mc3yRHNEeeMPMHjWz48zMog4o1eLxOEUD+9H+sxKKBvbbJMmt6XGRevnyyyD5XbUKpk8PFroQkawQ9gzweIIa0c3pDeyZvA0C7gx5fJGs5+5XEfwM3QMMAP5jZteb2R6RBpZCiWmlFHYvY0hPKOxeRmJaaZ0eF6mX9u2hf39IJIL6XxHJGqEmwO7+Ihv3kq3qJOB+D7wKtDWzn4QZg0gucHcHFiVv5cB2BC3pRkYaWIrEeuVT8noeI6dDyet5xHrl1+lxkTp5992g7nerrWDkSPj5z6OOSERClu6L4HYl6EhQYWFy31dVDzSzQQSzxLRo0eKgjh07piXAytavX89WW6W/TDpXxv3222/Iy1tC+/awaBGUlbWlXbudUjbe8uXLWVlWRsu8PLbddtsNr/fbb74hb8kS2hNkk2Vt29Jup9TF8emnn37n7u3q+3wzuwQ4G/gOGAtc4e5rk72Z/wMMCSfSxqOgoADGTiAxrZTiQfnBdh0eF6m1N98Mevv+4hfBzG/2VxiJ5KR0J8DV/Sbx6g509zHAGIAuXbr4J598ksq4qpVIJIjFYho3ReLxOEVF/TjttDJKSvIYP/6+lCUu8Xicon79GFpWRsmaNfz9zjtp3bo1sVhsw2OnlZVRkpfH+PtSFweAmf23gafYETjF3Tc6j7uvN7MTGnjuRqugoGCL/y41PS5So5kz4bjjYPvt4b77lPyKZLF0TzMuBHartN0BqE3vWcky8XicRKKU/v0H89ZbJ1NcPCG05CUej3PZxRt3A0iUllJYVsYQoLCsjETp/2pECwoKKJ4wgUWFhRRPCC+OFGpeNfk1sxsB3P2jaEISyXAvvhjM/O60U3C/U6eoIxKRFEp3AhwHzkp2g/glsNTdNyl/kOxWMfPbvn0JDz44iv32OzjU5LeoXz/al5RQ1O9/3QBi+fmU5OUxEijJyyNWZfnSgoICbr799kxIfgGOqWZf77RHIZIt3GH4cOjQAV54AXbbrcaniEhmC7UEwswmADFgRzNbCFwDNANw99HAZKAPMIdgSd5zwhxfMkMiUUphYRlDhgCU8dZbbzb4nPF4nERpKXPnzdsw00typnfDn8YnTCBRWkpxflAjmkgkGjxuOpnZH4CLgN3N7L1KD7UCXokmKpEM5x6UOjz+OKxZoxXeRHJEqAmwu/er4XEHCsMcUzJPLJZPUdE4IKj9HTTo4Aadr2LWt7CsjAe23po3mzeH1aspycujuNJMbxbUiD4MTAH+BgyttP9Hd99S9xURqc5TT8GYMTBxImyXc2vLiOQ0LYUsKVNR5xuLbXxVfnB/AolEKcXF+bRu4NKilet7WbOGV/r0YVHnzhtmerOFuy8FlgJb/I+miNTCv/4Fv/sddOsGK1dCixZRRyQiaaQEWFKios63sLAsOds7YZMkuGK7IaUI8XicV159lXnJ7ZuA87t1Y8SIEfU+Z2NlZi+7+2Fm9iP/655ScZm6u3vD/ichkiseeSRY4KJ7d5gyBdq0iToiEUkzJcCSElXrfBOJ0tBnYytKHzqXldGHoIdvH2Dl0qWhjtNYuPthya+too5FJGM98giccQb06AHPPAOt9OMkkovSv9qC5IRYLJ/bbmvOyJFw223NicXCX5nr3rvuorCsjMuAUqA9ML2aDg/Zxsz6mlmr5P2rzOxfZnZA1HGJZISf/xx++9tg5lfJr0jOUgIsKbN6tTN5cvA1bPF4nFemTmUU8DGwumlTZvTpkyl9fBvqanf/0cwOA44F7gNGRxyTSOP26qtBx4df/AIefRS22SbqiEQkQkqAJSUSiVKuuGINiQTJr6U1PqdO5y8t5Yo1a6jorXdYfj6TnnkmF5JfgHXJr8cDd7r7k8DWdTmBmR1nZp+Y2RwzG7qF4w4xs3VmdmoD4hWJ1q23wq9+BQ8/HHUkItJIKAGWlIjF8ikpyWPkSCgpyQu9BKJlmzbcRDD7+yHQtVu3UM/fyH1hZncBpwGTzaw5dfhZNrMmQAnB4hn7Av3MbN/NHHcj8GwoUYtEYLcJE2DwYDjlFOjbN+pwRKSRUAIsoYrH41x22cUAFBdPYNGiwpQsczx71qycuPBtM04jSEqPc/clwPbAFXV4fndgjrvPdfc1wCPASdUcVwQ8DnzTsHBFInLddewxZgycfnpw8dvWdfpDiYhkMSXAEprKSxwXFQWtam++ObzlhYcNG8YFv/0t7UtKeGXqVKY2b54zF75V5u5lwGfAsWZ2MbCTu9elxmRX4PNK2wuT+zYws12B36DaYslUH3wAw4ez6Jhj4MEHoVmzqCMSkUZEbdAkNKlsfRaPx7nzxhsZum5d1i94URMzuxQ4H/hXcteDZjbG3Ytre4pq9lW9UnEU8Cd3X2dW3eEbYhkEDAJo165dxi0v3VgtX75c72UIWt92G1926MC2L70UdSgZT9+T4dF72TgoAZbQtGzZhltuaQqUU1KSR3FxeLOyidJSCtatoyS5fUvTptx1wQU5lfhWch5wqLuvADCzG4GZQG0T4IXAbpW2OwBfVjnmYOCRZPK7I9DHzMrdfVLlg9x9DDAGoEuXLh6Lxer0QqR6iUQCvZf14A5DhsDhh0NBAcRiLNN7GQp9T4ZH72XjoBIICUU8HufBB0dx7LHl3HBDE/r3HxxqctqyTRsmAz2BEcBhJ5+cq8kvBDO46yptr6P6Wd3NeQPY08w6m9nWwOlAvPIB7t7Z3Tu5eyfgMeCiqsmvSKOyfj0UFsJNN4FmfEWkBpoBllBULn8YOXIdixaFe1FaxUVv2xMUpm6/886hnj/DjANeM7MnktsnA/fU9snuXp6sHX4WaALc6+6zzezC5OOq+5XMsm4dXHAB3HNPMAN8ww1RRyQijZwSYAlFLJZPUdE4oCz08oeKRS+aAYOBR5s3544cuuitMgtqEiYCCeAwgpnfc9z9nbqcx90nE7RQrryv2sTX3QfUJ1aRtFi3Ds45Bx54AK6+Gv7yF9hC3bqICCgBlhB16xZjxgwoLg63Nvfeu+7iijVr2Bu4GTioV6+cLX9wdzezSe5+EPB21PGIRG6rraBNG7juOrjqqqijEZEMoRpgabCK9mc9ekzmnXcSoZ+78pLHc5o357wLLgh1jAz0qpkdEnUQ6RaPx7ns0ouJx+M1HyzZb80aWLAgmO297TYlvyJSJ0qApcEq1/8WFpaFtuxxPB7numHDNlryOJdnfyvpSZAEf2Zm75nZ+2b2XtRBpVI8HqdoYD/af1ZC0cB+SoJz3erVcOqp0KMH/PijSh5EpM5UAiENlor632HDhnHvyJEcW17OTcDlwLy8PIo1+wvBEsY5JTGtlMLuZQzpCVBGYlp4PaYlw6xcCb/5DTz7LNx5J7RqFXVEIpKBNAMsDVZQUED//oOZOLFrKO3PKha9+GN5OeMJljqe2LUrxRPCW1I5k7n7f4G2wInJW9vkvqwV65VPyet5jJwOJa/nEeuVmxdB5rwVK+CEE6C0NOj4cOGFUUckIhlKCbA0WEUP4L59P+DBB0c1+M/T995114ZFL0YCzzZtytUjRij5TUquBPcQsFPy9qCZFUUbVWoVFBRQPHYCi/YopHis/iOUs66+GhIJuP9+OPfcqKMRkQymEghpsDCXQK7c8iwf+NtWW3HRkCFKeDbW0JXgMlJBQYG+D3LdX/4C+flw3HFRRyIiGU4zwNJgFUsgjxwJJSV5xGL1//N0orR0w0Vv84EjjjuOESNGhBVqtmjoSnAimeOHH+Dii4Pyh1atlPyKSCiUAEuDhL0Ecss2bbiJoOXZh0DXbt1CijSrVKwEN9zMhgOvUoeV4EQyxnffwVFHwd13w9tqey0i4VECLA1SUf4wfjwMHbqOlSsbtgTyyqVL6QMsIrj4beXScJdUzgbufjNwDvA98APBSnCjIg1KJGxffw09e8LHH0M8DocfHnVEIpJFVAMsDRJ2C7SWbdowmaDt2YPA+W3ahBBldjCzFsCFwM+A94E73L082qhEUuDLL6FXr2Chi2eeCWaBRURCpARYGqSgoIDXXhvMxIlx+vdv+EVKs2fN2mgGePasWSFEmTXuA9YCLxH0At4HGBxlQCIpsXw5lJfDv/+tmV8RSYnQSyDM7Dgz+8TM5pjZ0Goeb2NmT5nZu2Y228zOCTsGSZ+wW6A5UAq0T371EGLMIvu6e393vws4FTgi6oBEQvXdd+AOe+0FH32k5FdEUibUBNjMmgAlBLNT+wL9zGzfKocVAh+6+/5ADPiHmW0dZhySPmEvg9y1WzdWECx7vAJdBFfF2oo7Kn2QrPPZZ3DQQTB8eLDdVH+gFJHUCfs3THdgjrvPBTCzR4CTCC7or+BAKzMzYFuCC3n0YZ6B4vE4n302j0cfbQ6sbnANcDwepzQe5zfA9kAndBFcFfub2bLkfQNaJrcNcHdvHV1oIg3wySdBne/q1cEyxyIiKRZ2CcSuwOeVthcm91V2O0Ht4pcEF/Jc6u7rQ45DUiwej1NU1I8ePSazerUzY0Yfiovrv0JXPB6nqF8/fv7BB0wmKIGYnpdHLF9L3lZw9ybu3jp5a+XuTSvdV/IrmWn2bDjyyKDmN5EA/dVHRNIg7Bng6prxVy3jPBaYBRwF7AE8Z2YvufuyygeZ2SBgEEC7du1IJBIhh1qz5cuXa9zNeOihcZVWf1vDW29tTevWresV9/Lly/n79ddTWFbGEGAAML5zZwadd169z1nbcaN4n0UkqawsWNltq63g+edh772jjkhEckTYCfBCYLdK2x0IZnorOwe4wd0dmGNm84C9gdcrH+TuY4AxAF26dPFYLBZyqDVLJBJo3OotW7aMoqJS/tf+7Jx6xzxixAg+fOstPktuT23enDtGjUr5srdRvc9SP/F4nMS0UmK98rUkcrbIy4M774R99oE994w6GhHJIWGXQLwB7GlmnZMXtp0OVG0LsADoBWBmOwNdgLkhxyEhi8fjXHbZxRu6PBQUFFBcPIFFiwobVPoA8O+nnuJP5eWMJrj4bdeuXZXgyEbi8ThFA/vR/rMSigb2a3C3EYnYa6/B448H9wsKlPyKSNqFOgPs7uVmdjHwLNAEuNfdZ5vZhcnHRwPXAePN7H2Ckok/uft3YcYh4aqo9y0sLEsuehEkvBW3hp57/rx5jCJoaPspcMjOOzc45lxhZu3dfVHUcaRaYlophd3LGNIToIzEtFL9JylTvfwy9O4NHToEyW+zZlFHJCI5KPQ+M+4+mWAir/K+0ZXufwnoyqYMUrnVGQStzsJIPioufOtVVsYTBN80a7femvMuuKDB584hk4EDow4i1WK98imakFxx8PU8igfpV0hGmj4dTjgBdtsNpk5V8isikVGjRalRy5ZtuOWWpkB5KMsdV0iUlm648A1gdteu3DNihGb26qa6C0+zTkFBAYydQGJaKcWDVAOckUpL4aSTYPfdYdo0aN8+6ohEJIcpAZYtqljp7dhjy7nhhib84Q+DQ0s+WrZpw03J+5OB80MoqchBd0cdQLqEUXIjEXrhBejSBZ57Dtq1izoaEclxoS+FLNmlovxh/HgYOnQdK1eGtzDF7Fmz6AMsAvqgRS/qw93viDoGkS1auTL4+te/BvW/Sn5FpBFQAixbFIvlU1KSx8iRUFKSRywWTvlDPB7nlalTKSVY9GJq8+Za9EIk20ycCHvtBf/5D5jBtttGHZGICKASCKmFbt1izJgBxcUXhPYn6ERpKVesWcPewM3AQb166c/bItnkoYfgrLPg178GdXYRkUZGM8CyWZWXO37nnUSo566o//0Y+BDoquVPRbLHuHFw5pnBEsdTpkBrrdQtIo2LEmDZrMrtzwoLg/ZnYala/zt71qzQzp0rzOwYM7vbzLoltwdFHJIIxONw7rlw9NHw9NMqexCRRkkJsGxWqup/Ab76+usN9b+lgId25pxyEXAF0N/MjgK6RRuOCNCrF1x1VZAI5+VFHY2ISLWUAMsWBfW/fRq83HFl8Xicue+/zyqC9mermjTR4hf18627L3H3ywkWlzkk6oAkhz30ECxbBttsA9ddBy1aRB2RiMhmKQGWaqWy/vfeu+7iijVrGJ/c3vfgg3UBXP08U3HH3YcC90cYi+Sy66+H/v1h1KioIxERqRUlwFKtVNX/VrQ/G0VwAdyc5s3pfeKJoZw725nZfWa2dcW2uz9Z+XF3L05/VJLT3GH4cBg2DM44A668MuqIRERqRQmwVKti+eOw638r2p+NJih/OKhXL3r06BHKuXPA58BMM+tUeaeZ7Wdm90YTkuQs9yDh/ctfYMAAuO8+aKrOmiKSGZQAyyaqLn/cv3/4yx+r/VnduftVwDXAVDM73sxONrMEMA5IRBmb5KDFi+HBB+GCC+Cee6BJk6gjEhGpNf13XTZRufxh5Mh1LFqUuuWPZ8+axTHHHBPa+XPAi8C/gaeAb4DT3P3FaEOSnLJ+ffB1xx3hjTeCRS7Moo1JRKSONAMsm1D7s8bJzEqA94HlwD7A88AlZqZeU5Ie69fDhRfCJZcEJRDt2yv5FZGMpARYNlFQUED//oOZOLFrqOUPVdufrW7aVO3P6uZ9YG93H+run7j774GZwKtmtlfEsUm2W7cuWODi7ruhbduooxERaRAlwLKJihrgvn0/4MEHRxGPx0M553XDhm3U/uyw/Hy1P6sDdx/t7iur7PsHMJjg/xQiqVFeDmedFVzodu218Ne/auZXRDKaEmDZRNgt0OLxOEX9+vHzDz7YcAHcvLw8zf6GxN2fB3pGHYdksQED4OGH4YYb4Oqro45GRKTBdBGcbKKiBRqUU1KSR3Fxw2qAE6WlFJaVMQQYAEzs2pXiESM0+xsid/886hgki51+Ohx8MAweHHUkIiKhUAIsG6naAu0Pf2h4DXBF6zMI/k5/fkGBkl+Rxm7VKnj5ZTj6aDjhhKijEREJlUogZCMV5Q/jx8PQoetYubLhLdCqa30mUlfxeJzLLr04lJp0qUFZGZx4IvTuDfPmRR2NiEjolADLRlLRAk2tz6Sh4vE4RQP70f6zEooG9lMSnErLl8Pxx8Pzz8PYsdC5c9QRiYiETiUQspGCggJee20wEyfG6d+/4aUKFa3P1qHWZ1J/iWmlFHYvY0hPgDIS00pVRpMKy5ZBnz7w6qvBKm/9+kUdkYhISmgGWDYSdgu0RGmpWp9Jg8V65VPyeh4jp0PJ63nEeoW3OItU8s9/wmuvBV+V/IpIFlMCLBsJuwVaxQVwHwMfAl27dQshSsk1BQUFFI+dwKI9CikeO0H/iUqVgQPh3Xfht7+NOhIRkZRSCYRsJOwWaCuXLt3oAriVSxt+UZ3kpgJ1D0mNb74JZntHjYJf/AL23TfqiEREUi70GWAzO87MPjGzOWY2dDPHxMxslpnNNrMXwo5B6ueVV17ZqAVaGMsgt2zThskEF8BNTm6LSCPx1VfQsyfMnBkkwiIiOSLUBNjMmgAlQG9gX6Cfme1b5Zi2wB1Agbv/HOgbZgxSf++992bKW6BpBlikkfjiC4jF4L//hSlToFevqCMSEUmbsGeAuwNz3H2uu68BHgFOqnLM74F/ufsCAHfXtEMjsd9+B4faAi0ej/NSaemGFmhTmzcnlq+Ll6R6Vfv8qu9vCn3xBRxxRDAD/OyzcOSRUUckIpJWYSfAuwKVl2RdmNxX2V7AdmaWMLO3zOyskGOQeurRowf9+w9m4sSuoZQ/3HvXXfypvJzRBOUPu3btqhpOqVbVPr/Dhg1T399U2mGHYGnjqVOhR4+ooxERSbuwL4KzavZVXfegKXAQ0AtoCcw0s1fd/dONTmQ2CBgE0K5dOxKJRMih1mz58uU5Ne60adO4//5/UFS0muLi/5CXl0ePBnw4frd4MaOAwcCnwO5Nm1b7unLtfZZNVe3zO3FKXH1/U2HOnCD53W67oNWZiEiOCjsBXgjsVmm7A/BlNcd85+4rgBVm9iKwP0GOtIG7jwHGAHTp0sVjsVjIodYskUiQS+PeeeetFBWtZsgQgNUsWvRVg+I4slcv3n/tNSYDK5Lb1Z0v195n2VSsVz5FE8YBZZS8nkf/8wsouXvuhu3iQSqdabAPPwzqfA86CJ5+OupoREQiFXYC/Aawp5l1Br4ATieo+a3sSeB2M2sKbA0cCtwSchxSD0ENcClQFkoLtNmzZvEbYHugE7oATjavoKAAxk4gMa2U4kHBYimHHnroRtvSAO+9B0cfDU2awMiRUUcjIhK5UBNgdy83s4uBZ4EmwL3uPtvMLkw+PtrdPzKzfwPvAeuBse7+QZhxSP116xZjxgwoLr6gQUlHPB7nlalTaUZQAvFo8+bcoQvgZAuq9vlV39+QvP02HHMMtGwJzz8Pe+0VdUQiIpELfSEMd59McM1T5X2jq2z/Hfh72GNL/cXjcUaPvo6iotWUlOQBFzTofPfedRdXrFnD3sDNwEG9eimZEUk392B1t223DZLfPfaIOiIRkUZBK8EJECyB/L/632AJ5PomrBXtz14nmP2d07w5d1zQsIRaROrBDB5/HLbaCn7606ijERFpNEJfCU4yUyyWz6hRzRg5Em67rXmDegCr/ZnUR9++fenQfgf69tXaOA32wgtwySWwfj107qzkV0SkCiXAssGaNc7kybB6ddXOdXXz1ddfMwr4mKC1R/uddw4hOslmffv25YUpj3HJwd/zwpTHlAQ3xNSp0Lt38HXJkqijERFplJQACxCUQAwZUk4iAVdcsYZEorRe54nH48x9/31WEcz+rm7alPNU/iA1mPnS81wegyE94fJYsC31MGUKnHAC/OxnkEjA9ttHHZGISKOkGmABghKIwsKxwOoGtUBLlJZudPHbYflqYSU1+9XhR3HTlMcAuCkBR/Y+KtqAMlE8Dn37ws9/Ds89Fyx4ISIi1dIMsBCPx0kkSjnyyFNZtKiQ4uIJ9U5aW7Zpw00E5Q8fAl27dQsxUslWEydO5Mjep3Lbm9tzZO9TmThxYtQhZZ4WLeCXv4Rp05T8iojUQDPAOS4ej1NU1I/CwjImTmxOScmjDZqxXbl0KX2ARUAftPiF1J6S3nqaNy+40C0/P+j3a9WtSC8iIpVpBjjHJRKlFBaWMWQIFBWtrnftb4WWbdowGWhPUAPcsk2bMMIUkercd1+wsMUzzwTbSn5FRGpFM8A5LhbL56KLgtrfUaOaMXp0w5c/rjwDPHvWrIYHKSKbGjsWBg2Co46CWCzqaEREMopmgIXVq4P2Z2vWNKz9GQQt0EoJZoBLgYafUXJFPB7nsksvJh6PRx1K41dSAuefD8ceC089BdtsE3VEIiIZRQlwjkskSpNtz0i2Qat/CYRaoEl9xeNxigb2o/1nJRQN7KckeEvefBMuvhgKCmDSJGjZMuqIREQyjhLgHBeL5VNSksfIkVBc3LAV4CpaoI1PbqsFmtRWYlophd3LGNITCruXkZjWsFr0rHbwwfDoozBxIjRvHnU0IiIZSQlwDqtof9a//2AWLSrkwguvblDCqhZoUl+xXvmUvJ7HyOlQ8noesV4Nq0XPSiNHwltvBff79oWtt442HhGRDKYEOEdVtD9r376EBx8cRSyWT48ePRp0TrVAk/oqKCigeOwEFu1RSPHY+vehzkrucNVV8Kc/wf33Rx2NiEhWUBeIHFW5/RmUkUiUUlBwaoPOWdEC7XLgQeB8tUCTOigoKFDiW5U7DBkCN90UXPR2yy1RRyQikhWUAOeoWCyfoqJxQFmDlj6urGoLNM0AizSAOwweDLfdBoWFwdet9Ec7EZEw6LdpjiooKKB//8FMnNiV/v0HN3jmLR6P81Jp6YYWaFObNyeWrzrOxsrMjjOzT8xsjpkNrebxM8zsveRthpntH0WcOa28HObOhcsug+JiJb8iIiHSDHCOisfjPPjgKAoLyygpmcuhhx5K69at632+e++6iz+Vl7M3cDOwa9eu+nN2I2VmTYAS4BhgIfCGmcXd/cNKh80DjnT3H8ysNzAGODSVccXjcRLTSon1yvHuIevW0XT5cmjWDP71L2jaVCu8iYiETFMKOapyDXBhYVmDl0B2YBRBB4hPgfY779zwICVVugNz3H2uu68BHgFOqnyAu89w9x+Sm68CHVIZkPoAJ5WXw4ABdBs8GFauDJJgJb8iIqHTDHCOqrwE8m23NeeOOxpWrtC1WzcSkyczGViBWqA1crsCn1faXsiWZ3fPA6ZU94CZDQIGAbRr145EIlGvgB66f9yGPsBQxkP3j2vQXyQykZWXs8+IEeyUSLDwzDNZ9NprUYeUFZYvX17v70v5H72P4dF72TgoAc5hFUsgr17d8AWLVy5dym+A7YFO6AK4Rq66KcVqvwnMrCdBAnxYdY+7+xiC8gi6dOnisVisXgEtW7aMooGlQBklr+dRPPYc6nuujLRmDZx+OiQScNNNLDrooNx6/SmUSCT0XoZA72N49F42DiqByFGVl0AOvjasBKKiBVp7gmWQW6oFWmO2ENit0nYH4MuqB5nZfsBY4CR3X5zKgHK+D/Bll8ETTwSdHv7f/4s6GhGRrKcZ4BwVdhs0tUDLKG8Ae5pZZ+AL4HTg95UPMLOOwL+AM93903QEldN9gIcOhV/+Evr3jzoSEZGcoBngHFR1CeTi4obNuKkFWmZx93LgYuBZ4CPgUXefbWYXmtmFycP+DOwA3GFms8zszYjCzV4rVsCNN8K6ddChg5JfEZE00gxwjqlYAjlof5bX4OQX1AItE7n7ZIJqlcr7Rle6PxAYmO64csayZXD88TBjBhx2GDRwGXIREakbzQDnmLDbn4FaoInUyZIlkJ8PM2fChAlKfkVEIhB6AlzTClOVjjvEzNaZ2alhxyCbF4vlc9ttzRk5Em67rTmxWMNLFbp268YKUAs0kZp8/z0cfTS8/TY89hicdlrUEYmI5KRQSyBqucJUxXE3EtQgSpqF2f4Mggvg1AJNpBY+/TRY3viJJ4ISCBERiUTYM8A1rjCVVAQ8DnwT8vhSg7Dbn8XjcV6ZOlUXwIlsyapVwddf/hLmz1fyKyISsbAT4OpWmNq18gFmtivwG2A0knaxWD4lJXmMHAklJXkNLoFIlJZyxZo1jCYogTioVy9dACdS2RdfQLducPfdwXaOrXAnItIYhd0FojYrTI0C/uTu62wLa9yHtcRqQ0S1XGEqx23dujWHH/4bxo+fweGH/5rWrVtvGKs+436zZAkPApcDHwL5221X53Nk4/ssAsCCBXDUUfD117D33lFHIyIiSWEnwLVZYepg4JFk8rsj0MfMyt19UuWDwlpitSGiWq4wlePG43FeeumJZBu0rznttNM2zNjWZ9xRf//7RgtgbN+2bZ3PkY3vswjz5gXJ7w8/wHPPBeUPIiLSKIRdArFhhSkz25pghal45QPcvbO7d3L3TsBjwEVVk19JnTDboGkBDJHNWLYMjjwSli6FadOU/IqINDKhJsC1XGFKItSyZRtuuaVpKDXAFQtgVNT/agEMaYi+ffvSof0O9O3bN+pQGq51axgyBKZPh4MOijoaERGpIvQ+wO4+2d33cvc93H1Ect/oyqtMVTp2gLs/FnYMUr14PM6DD47i2GPLueGGJvTvP7hBCasWwJCw9O3blxemPMYlB3/PC1Mey9wk+IMPggUuAC6+GPbfP9p4RESkWloJLodUlD+MHw9Dh65j5cqG9evVAhgSlpkvPc/lMRjSEy6PBdsZZ9YsiMXgvPNg3bqooxERkS0I+yI4acRisXyKisYBZZSU5FFc3LB6XS2AIWH51eFHcdOU4I9BNyXgyN5HRRtQXb35ZrC88bbbQjwOTZpEHZGIiGyBZoBzSEFBAf37D2bixK4NLn/QAhgSpokTJ3Jk71O57c3tObL3qUycODHqkGpv5kzo1QvatoUXX4Sf/SzqiEREpAaaAc4hFTXAQQu0uRx66KH1ToLvvesurlizhr2Bm9ECGNJwGZX0VjZmDOy0Ezz/POy2W83Hi4hI5JQA55DKLdAgaIFWn6S1ov3Z68BgYE7z5txxwQXhBivS2K1fD1ttFSTAP/wQJMEiIpIRVAKRQ8JaBlntzyTnPfssHHIIfPMNNGum5FdEJMMoAc4x3brFmDGjD8XFE+qdtH719ddqfya56+mnoaDgfzPAIiKScfTbO0fE43GKivrRo8dk3nkn0aDzzH3/fVYRzP6ubtqU81T+ILniiSfglFNgv/2CFd523DHqiEREpB6UAOeIsJZArrj4bXxy+7D8fJU/SG54+mno2xcOPhimToXtt486IhERqSclwDkiFsvnttuaM3Ik3HZb83rV/1a0PhtFUP4wp3lzzf5K7jj4YDjrrKD+t02bqKMREZEGUAKcQ1avdiZPDr7WR8Xsb8XFb2p9Jjnhuedg7Vpo3x7uvRdatYo6IhERaSAlwDkikSjliivWkEiQ/Fq3EoiK1mej0Oyv5JA77wxWeBs1KupIREQkROoDnCMaugxyReuzioUv1PpMst6tt8LgwXDiiXDJJVFHIyIiIdIMcI5o6DLIDmp9Jrnj738Pkt9TToHHHoPmzaOOSEREQqQEOEdULIPct+8HPPjgKOLxeJ2e37VbN1YQ1P6uSG6L1Fc8HueySy/e6Puwun2R+PJLuPZaOP10eOQR2HrraOMREZHQqQQiRzR0GeTZs2bxG2B7oBOwcunSlMQp2S8ej1M0sB+F3csomjAOxk4A2GRfZCU2u+wCr70Ge+0FTfUrUkQkG+m3e45o2bINt9zSFCivcw1wRfuzZsBg4NHmzbkjv37LKIskppVS2L2MIT0BykhMCy7IrLovrQmwOwwdGnR6+OMfYd990ze2iIiknRLgHFBR/nDsseXccEMT/vCHutUAJ0pLuWLNmg0XwKn9mTRErFd+MMtLGSWv51E8KPjPVHX70sI9SHpvvRUuuijYNkvf+CIiknZKgHNA5fKHkSPXsWhR3coXWrZpw03A5cCHwPmq/5UGKCgogLETSEwrpXhQpZUEq9uXauvXw8UXB+3OLr0UbrlFya+ISA5QApwDGtoCbeXSpfQBFgF9SF/979q1a1m4cCGrVq1KyfnbtGnDRx99lJJzA7Ro0YIOHTrQrFmzlI2RqQoKCjZJcqvbl1LucOGFcPfdMGQI3HCDkl8RkRyhBDiLxeNxEolSYrF8iosnkEiUUlxc99m1lm3aMJlgBvhB4Pw0LQO7cOFCWrVqRadOnbAUJCY//vgjrVK0qpe7s3jxYhYuXEjnzp1TMoY0kBnstx9cdVXQ9UHJr4hIzlAbtCwVj8cpKupH+/YlFBX1A+Dmm2+v1wzb7FmzNpoBnj1rVpihbtaqVavYYYcdUpL8ppqZscMOO6Rs9loaYO1aeO+94P7FF8N11yn5FRHJMUqAs1Tlut/CwrI6L31c2Vdff00p0B4oJVgUI10yMfmtkMmxh23YsGEccuAvGDZsWLSBrFkT9Pf99a+Dfr8iIpKTVAKRheLxOJ99No9HH20OrK5X3W/lc819/33WESyCsbppU8674IIww5UsN2zYMO6+9Xouj8FNt34AwIgRI9IfyOrV0LcvPPUUjBoV9PsVEZGcpBngLFNR+tCjx2RWr3ZmzOhDcXH9FxWoaIE2Prl9WH4ar9CXrFA6Jc7lMRjSEy6PBdtpt3IlnHRSkPzecUfQ8UFERHKWZoCzzMYrvq1h0aLODUpY1QJNGiq/d8GGmd+bEnD+pRH8B+r226G0FMaOhfPOS//4IiLSqIQ+A2xmx5nZJ2Y2x8yGVvP4GWb2XvI2w8z2DzuGXBaL5VNSksfIkVBSkkcs1rAFBapeAJeLSyDfeeedXHTRRRu2r7rqKs4888wII8osI0aM4PxLr2Til105/9Iroyl/+OMf4fnnlfyKiAgQcgJsZk2AEqA3sC/Qz8yqrik6DzjS3fcDrgPGhBmDQLdusQaXPkBQTvFSaemGC+CmNm9OLAeXQD777LN56qmnWLJkCU8//TTPPPMMY8bo27YuRowYwRtvv5/e5HfpUujfP7jYrWlTiMXSN7aIiDRqYc8AdwfmuPtcd18DPAKcVPkAd5/h7j8kN18FOoQcQ86qXP/7zjuJBp/v3rvu4k/l5YwmuABu165dc7L+Ny8vj379+jFs2DAuueQSHnvsMVq2bBl1WLIl338PRx8Njz4K774bdTQiItLIhF0DvCvweaXthcChWzj+PGBKyDHkrI3rf4PWZw1JWL/6+mtGAYOBT4FDdt45jDBTKh6PkygtJRbyxXrnnnsu++yzD08++SR77LFHaOeVFPjuOzjmGPjwQ/jXv6B376gjEhGRRibsBLi6xqfVto01s54ECfBhm3l8EDAIoF27diQSiZBCrL3ly5dn1Lg77PATiouD1mfFxc258MKf1Ok8lcd95ZVX+Ozdd1lPMPu7qkkTuv/61yl5Pzb3etu0acOPP/5Y6/NMnjyZIeecQ+HKlRTdey8rx42jT58+mz1+3bp1tT7/1VdfzY477siPP/644Tnz5s3jpptuYtmyZTzwwAPVPm/VqlWRfA/lrK+/DmZ+58yBeByOPTbqiEREpBEKOwFeCOxWabsDsEm3eTPbDxgL9Hb3xdWdyN3HkKwP7tKli8ciqN9LJBJk0rjLli3jwAN7MWMGlJRcUOcZ0Mrjxh97jCHl5ewN3AwcfuyxKVvEYHOv96OPPqrTUsWvvfQShStXMgRg5Upee+klfve73232+NouhfyPf/yDdevWMXHiRK655hrOOOMMAPbbbz/uv/9+Tj311M2ep0WLFhxwwAG1fg3SQE2awDbbwNNPQ69eUUcjIiKNVNg1wG8Ae5pZZzPbGjgd2Kjpp5l1BP4FnOnun4Y8fs4Ku/63ov3ZxwTtz7pmQPuzWH4+JXl5jARK8vJCuWDv+eefZ9y4cdx3333EYjGWLVvGrDQtBS118NVXwSpvO+4IM2cq+RURkS0KNQF293LgYuBZ4CPgUXefbWYXmtmFycP+DOwA3GFms8zszTBjyFVhLn0Mmdn+rKCggOIJE1hUWEjxhIZ1wABYsGABAwcOZOLEiRtmeC+99FJGjRoVQrQSmvnzoUcPOP/8YFtLUIuISA1CXwjD3ScTlI1W3je60v2BwMCwx811LVu24ZZbmgLlDV76+J677uLl0lKaE1wA92jz5tyRIe3PCgoKQrv4rWPHjsydO3ejfQMGDGDAgAEALF68mGHDhvHOO+/wt7/9jf/7v/8LZVypgzlz4Kij4Mcf4eKLo45GREQyhFaCywLxeJwHHxzFsceWc8MNTfjDHwbXKwl85ZVXGHP99XQuK+NPsKH+96BevXKy/VlNdthhB0aPHl3zgZIaH38clDqsXg3Tp0MGlOmIiEjjEPpKcJJ+FeUP48fD0KHrWLmyfuUK/37qKQrLyrgMGEVQ/zsvL4/zLrggvGBFwrBuHfzmN1BeDomEkl8REakTzQBngVgsn6KicUBZvcsf4vE4H731Fp8RlD2sbtqUGfn5FF9Q924SIinXpAncdx+0agX77BN1NCIikmGUAGe4eDxOIlFK//6DWbRoKcXF9VsAIlFaulHbs8Py85n0zDOhxyvSIG+9BS++CH/8I3TvHnU0IiKSoVQCkcEqWp+1b1/Cgw+OIhar/+pnmdj2TBqveDzOZZdeTDwer/ng2nr11aDm99ZbYdmy8M4rIiI5RwlwBguz9dnKpUszru2ZNE7xeJyigf1o/1kJRQP7hZMEv/xysLzxjjsGM8CtWzf8nCIikrOUAGewWCyf225rzsiRcNttzYnF6t+qrGWbNkwG2hP0sGvZpk1YYUqOSUwrpbB7GUN6QmH3MhLTGtaTmunTgyWNd90VXngBOnYMJ1AREclZqgHOcKtXO5MnB18bIhMXvpDGKdYrn6IJyYsyX8+jeFADe0jPmwe77w7PPQft24cSo4hIY7F27VoWLlzIqlWrog6l0WrRogUdOnSgWbNmoZ1TCXAGSyRKueKKNQwZAiNHriGRKK1XDXA8HuelDF34QhqfgoICGDuBxLRSigfVvy6dJUugbVs491zo3x+23jrMMEVEGoWFCxfSqlUrOnXqhGkly024O4sXL2bhwoV07tw5tPOqBCKDxWL5lJTkMXIklJTk1bsE4t677uJP5eWMJih/2LVrV7U+q+TOO+/koosu2rB91VVXceaZZ0YYUeNXUFDAzbfeXv/vo0mToFMnmDEj2FbyKyJZatWqVeywww5KfjfDzNhhhx1CnyFXApyhNm5/Vkhx8YR6Jxtfff31hoUvPgXa77xziJFmvrPPPpunnnqKJUuW8PTTT/PMM88wZsyYqMPKXhMnQt++QX/fffeNOhoRkZRT8rtlqXh/lABnoDDbn8Xjcea+/z6rCGZ/VzdtqpXfqsjLy6Nfv34MGzaMSy65hMcee4yWLVtGHVZ2eughOP10+OUvobQ0KIEQEZGUatKkCd26daNr16707duXsrKyTY4ZMGAAnTt3plu3bhx44IHMnDmz1udPJBKccMIJNR4zo+KvfmmgBDgDhdX+LB6Pc92wYVyxZg3jk/sOy29AzWYjEI/HueyykPvPAueeey533HEHo0aNYo899gj13Nlon332YdsWTdinLqu0vfwynHkmHHkk/PvfwSpvIiKSci1btmTWrFl88MEHbL311owePbra4/7+978za9YsbrjhBi4IebJMCbBsVkVy17JlmwbX/sbjcYr69ePnH3ywYQGMz5o3z+jZ38oz40VFIfWfTbr22mtp164d5eXlG/ZNmjSJ888/n5NOOonS0ga2+soi++yzD4s//5g/H72exZ9/XPsk+Ne/hn/8A55+GrbZJrVBiohItQ4//HDmzJmzxWOOOOKIao8ZMGAAF154IYcffjh77bUXTz/99CbHfP/995x88snst99+/PKXv+S9995j/vz5jB49mltuuYVu3brx0ksvhfZ6NkddIDJERXJXWFhGSUleg5c+vveuuygsK2MIMACY2LUrF55+ekbP/laeGYeyenfFqOof//gHq1at4tFHH+Waa67hlFNOAeDkk0/m5JNP5ocffuDyyy8nX50zAPh83qf8+WgY0jPYvnbqp1t+wrhxcNRR8NOfBksci4jIFsXjcRLTSon1CvevtuXl5UyZMoXjjjtui8c99dRT/OIXv6j2sfnz5/PCCy/w2Wef0bNnz00S5WuuuYYDDjiASZMm8fzzz3PWWWcxa9YsLrzwQrbddlsuv/zy0F7PligBzhBVk7tFi5Zy88231+tcFW3PXk9uT23enDtGjKB1hq+uFYvlU1SU7D9bkkdxccMT0ueff55x48Yxc+ZMWrVqxbJly5g1axbdKi0V/de//pXCwsIGj5Utduu8FzclPgbgpkSwvVn/+AdcfjkUFcFtt6UnQBGRDFax2mZh97Kg5/rY+l8EX2HlypUbPtcOP/xwzjvvvGqPu+KKK/jrX/9Ku3btuOeee6o95rTTTmOrrbZizz33ZPfdd+fjjz/e6PGXX36Zxx9/HICjjjqKxYsXszSCtQeUAGeIMJO7irZnewM387+2Z4lEIqRooxH8AphAIlFa75nxyhYsWMDAgQN55plnaJWsR7300ksZNWoU48ePx90ZOnQovXv35sADDwzhFWSHjz76iH322Ydrp37Kbp334qOPPqr+wOuvh2HD4LTTgkRYRERqVHm1TQhW22zo511FDXBl55xzDu+88w677LILkydPBoIa4FNPPXWL56rasaHqtvumC3dF0QVDCXCGKCgo4LXXBjNxYpz+/Qsa9M3uwCiCRS8+BQ7JorZnBQUNe28q69ixI3Pnzt1o34ABAxgwYAAAxcXFTJ06laVLlzJnzhwuvPDCUMbNBptNegHc4S9/CW79+wclEE31q0hEpDZCX21zM8aNG1ev502cOJGzzz6befPmMXfuXLp06cKrr7664fEjjjiChx56iKuvvppEIsGOO+5I69atN/yVNV30qZMh4vE4Dz44KlkDPJdDDz203ole127dSEyezGRgRXJb6u6SSy7hkksuiTqMzLNqFTz1FJxzDtx9NzRpEnVEIiIZI7TVNlOkS5cuHHnkkXz99deMHj2aFi1abPT48OHDOeecc9hvv/3Iy8vjvvvuA+DEE0/k1FNP5cknn6S4uJjDDz88pXEqAc4QDb3AKx6PkygtJZafz8qlS/kNsD3QCVgZQe2N5CB3WLsWWraE6dNh221hKzWiERGpqzD/2gmwfPnyGo8ZP358rc7Vo0cPbrnllo32xWIxYrEYANtvvz1PPvnkJs/ba6+9eO+992o1Rhj06ZMhGrLscUXLs/YlJRT160fLNm2YnpdHe2B6Xh4xdS+QEPzn0083v0DI+vXBhW4nnxwkwa1bK/kVEZHIaAY4g3TrFmPGDCguvqBO//Or3PKMsjIWLV1K8YQJJEpLKc7whS+k8dilDawpX0XLli1ZuXLl/x5Yvx4uuADGjg06PqjeV0QkK9V2lrgx0CdRBqjaAxgu2LC/oqxhc0lsPB7nlalTN7Q8u615c+5IHq/EV8LUvhWctj9c8+yq/+1ctw7OOw/uuy/o+HDddaA170VEJGJKgDNAdfW/AEX9+lFYVkbRuHEwofo+gInSUq5Ys2ZDy7ODevXKqMTX3SNpjxKG6lq9ZLNFPwZ9f2la6YKHSy4Jkt9rr4Wrr44qNBGRRi2TP+vSIRWfpyrCa4ReeeUVLrvs4g1L+Vat/23Zsg3XDRu2oayhsKyMxGaW4o3l51OSl8fHwLy8vIxa6rhFixYsXrw4IxNJd2fx4sWbXP2azb5cCj+ua7Fx+cP558Mttyj5FRHZjEz+rEuHVH2eaga4kYnH44wefR1FRauTC19M2GiBh/792/DgqFH0LCvjpuRzSvLyKN7MhWwFBQWQofW+HTp0YOHChXz77bcpOf+qVatSmqC2aNGCDh06pOz8jc2ee+3FJ598AqtXw+OPw+9/D926BTcREalWqj/rskEqPk9DT4DN7DjgVqAJMNbdb6jyuCUf7wOUAQPc/e2w48hUiUQpRUWrN2l3VnE7+fjjN8z8DgAmdu1K8YgRW0xsM7Xet1mzZnTu3Dll508kEhxwwAEpO39jlrKf05Ur4be/hSlTYK+94OCDww9eRCSLpPqzTqoXagJsZk2AEuAYYCHwhpnF3f3DSof1BvZM3g4F7kx+FYJyh8LCscDqTZY8rnpB29TmzbmjhuRXpKpU/ZyaOxQUwLRpMGaMkl8REWm0wq4B7g7Mcfe57r4GeAQ4qcoxJwH3e+BVoK2Z/STkOBqteDy+UX1vVQUFBRx55KlM/GdX+vcfvFFyW3FB22hgMpl3QZs0Gin5OW25cCE8/3ywtPH556cmchERkRCEnQDvCnxeaXthcl9dj8lKFe3M2rcvoaioX7VJcDwe54XHHqPv2x/w4KhRGx2TyRe0SaOSkp/TJitXwgMPwNlnhxKkiIhIqoRdA1xdD4+qlzXW5hjMbBAwKLm52sw+aGBs9bEj8F1YJzNjt112YadHH4V168o4+eSTvnHfKMnAYLddYKdHgXVlZZx80knf+MaJSJuh0NrLypaddNJJYa9hHOrr1bib1SWCMStL3c/pGWd8wBlnNDA8IbrvzWyk9zIceh/Do/cyPPX+PA07AV4I7FZpuwPwZT2Owd3HAGMAzOxNd097QWGU4y7Msdeba+Ome8wqsurnNBvpvQyP3stw6H0Mj97L8DTk8zTsEog3gD3NrLOZbQ2cDlT9O38cOMsCvwSWuvtXIcchIpunn1MREclpoc4Au3u5mV0MPEvQXuled59tZhcmH6+4fqsPMIegvdI5YcYgIlumn1MREcl1ofcBdvfJBB+elfeNrnTfgcI6nnZMCKHVh8bVuNk07gZZ9nOajfRehkfvZTj0PoZH72V46v1empbeExEREZFcEnYNsIiIiIhIo9aoEmAzO87MPjGzOWY2tJrHzcxuSz7+npkdmKZx9zazmWa22swuD2PMWo57RvJ1vmdmM8xs/zSNe1JyzFlm9qaZHZaOcSsdd4iZrTOzU9MxrpnFzGxp8vXOMrM/p2PcSmPPMrPZZvZCGOOmWlQ/p9kmqp//bBTV75ZslK2/t6JQi5/xNmb2lJm9m3wvda1FNczsXjP7xjbTDrfenznu3ihuBBfjfAbsDmwNvAvsW+WYPsAUgh6lvwReS9O4OwGHACOAy9P4en8NbJe83zuNr3db/lcesx/wcTrGrXTc8wT1qaem6fXGgKcj+H5uC3wIdKz4PgszhlTcovo5zbZbVD//2XiL6ndLNt6y9fdWI34vrwRuTN5vB3wPbB117I3tBhwBHAh8sJnH6/WZ05hmgKNaRrnGcd39G3d/A1jbwLHqOu4Md/8hufkqQS/WdIy73JPfVcA2VLMAQirGTSoCHge+CWHMuowbttqM+3vgX+6+AILvszTE1VBa7jwcUf38Z6Oofrdko2z9vRWF2ryXDrQyMyOYePoeKE9vmI2fu79I8N5sTr0+cxpTAhzVMspRLc1c13HPI/gfTlrGNbPfmNnHwDPAuekY18x2BX4DjCY8tX2ff5X8M9QUM/t5msbdC9jOzBJm9paZnRXCuKmm5c7DEdXPfzaK6ndLNsrW31tRqM17eTuwD8EiQ+8Dl7r7+vSEl1Xq9ZkTehu0BghtedYUjJsKtR7XzHoSfACGUYtbq3Hd/QngCTM7ArgOODoN444C/uTu64L/EIeiNuO+DfzU3ZebWR9gErBnGsZtChwE9AJaAjPN7FV3/7SBY6dSVD+n2Saqn/9sFNXvlmyUrb+3olCb9/JYYBZwFLAH8JyZveTuy1IcW7ap12dOY0qAQ1ueNQXjpkKtxjWz/YCxQG93X5yucSu4+4tmtoeZ7ejuDVm7vDbjHgw8kvyA2hHoY2bl7j4pleNW/mXj7pPN7I40vd6FwHfuvgJYYWYvAvsDjfmDJKqf02wT1c9/Norqd0s2ytbfW1GozXt5DnBDsuRwjpnNA/YGXk9PiFmjfp85URc3VypibgrMBTrzv4Lxn1c55ng2LnR+PR3jVjp2OOFdBFeb19uRYCWuX6f5ff4Z/7sI7kDgi4rtdLzPyePHE85FcLV5ve0rvd7uwIJ0vF6CP31NSx6bB3wAdA3r3zoVt6h+TrPtFtXPfzbeovrdko23bP291YjfyzuB4cn7Oyc/a3eMOvbGeAM6sfmL4Or1mdNoZoA9ouVZazOumbUH3gRaA+vNbDDB1Zz1/jNFLV/vn4EdgDuSMxfl7n5wfcesw7i/Bc4ys7XASuB3nvwuS/G4oavluKcCfzCzcoLXe3o6Xq+7f2Rm/wbeA9YDY9292jYvjUVUP6fZJqqf/2wU1e+WbJStv7eiUMvvy+uA8Wb2PkHy9idv2F8es5KZTSDo1rSjmS0ErgGaQcM+c7QSnIiIiIjklMbUBUJEREREJOWUAIuIiIhITlECLCIiIiI5RQmwiIiIiOQUJcAiIiIiklOUAKeYma0zs1lm9oGZTTSzvBSNc7CZ3Za8HzOzX9fjHIMrlrU0s72Tcb9jZns0MLZuyRXWKrYLzGxoPc/VLtmCR0RERKRelACn3kp37+buXYE1wIW1eZKZ1alHs7u/6e6XJDdjQJ0S4OR45wIPJ3edDDzp7ge4+2eVjjMzq+v3TTeCHn0Vscbd/YY6nqPiud8CX5lZj/o8X0REREQJcHq9BPzMzLY3s0lm9p6ZvZpc7hQzG25mY8ysFLjfzH5qZtOSx00zs47J4/omZ5TfTS5DWTHr+7SZdSJIsv+YnME93MzmmVmz5HGtzWx+xXYlRwFvJ5t39wEGAwPNbLqZdTKzj8zsDuBtYDczu9PM3jSz2Wb2l4qTmNkhZjYjGdvrZtYGuBb4XTKe35nZADO7PXn85l7jeDO7LXmuuWZ2aqVYJwFnhPjvIiL1ZGZ/SP5uqNj+q5k9EMJ50/LXs+RYM5Jf25rZRfV4ftpi3UIMm8Re8bqS95fX8XwtzeyF/9/e+cdsVZZx/PPd2wgyzKRf8k+G0g8oghw5Zw5IsyKNtkwt+6UN12zhMtowGrPIWWPZTP+whaggazhE0jRHqxSjiQQv6KumLtPadDOQVH4UZt/+uO/n5XB2nvP8kPcHe67PdrbnnOe6r/O937P3Pte5rvs8t6S+vL8g3wdWD6XODn2NkbSx04RREEAEwMNG/gf9JPAw8H2g3/Y04LvAyoLpScA8218ArgNWZrvVwM+yzRLg47Y/CHy6eB7bTwPXAz/Nmef7gXtJSwUCnA/cZvuVksRTga3Zx90FH3Py9+/JWmbYfgZYnFelmgbMkjRN0hhgDXBp1nYGsDfrXZP1rCmdt1kfAY4DPgKcBRQzxn8GTiMIgtHAzcDZObA5izTWXHwY/HZbPeu4SmW7UTE7Bug4AGYYtdZwDCXthX51w0XAOtuv5v1LgLm2B5MPXeo/bDptHyAty3xeN+2D3iYC4KFnnKTtpKDt78ANpKBuFYDt3wMTcqYU4A7b+/PnUzg4JWFVbgewibR84nzSEoutWM7BpQEvBG6ssDkO+GeNj2dsP1DYP1fSNqAfmApMIQXJz9nekvv2ku3/ttDWrI8A623/z/ajpHXSGzwPTGzhNwiCYcD2PuCXwJWkB9hzCmPY4eJ+4EQASZflTOuA0rL0NKlSVdkdJemuXKEakHRePt7Ijv4IOCFnc5dJWirp0oYISVdKWkA9Ra1fzJWw7ZJ+LqmvidYv5yrYjmL2vEX7X+QK3AZJ48raS/06hCq/FWYXAL/K9tcDk4A7JH2rrD/brJe0NWu6uHCuct9qdba4vuU+Q1QEgy6JssHQs9/29OIBSaqwa6xJvbfGlwFsf13SyaRMy3ZJ02vaYHtTHkBmAX1N1m3fD4ytcTOoS9K7gIXATNu7Jd2U26rQj24ptv9P4XPxbzY26w2CYHSwAniMVL36ayvjTihUz+6RdBLpIf5k0piwWdJ9wG7SA/iFti+psZsEPGv7U9n3m0qnWwS8vzFmK00pWwdckzOd5wMfblPr+0iZyVNtv5IDxguAjSWtU4HF2W6npGOzr7r2k4HP254v6Vbgs2XtNRqb+V1ZsBkDTMoVxcY95xPAHOCNwE8a+guuL7L9Qg5Mt0i6DXhHRd+ObqazxfWt6vMtwAAws67PQVBFZIBHho3kJ1ZJs4Gdtl+qsPsTacAl2/8xtznB9mbbS4Cd5CfwAi8D40vHVpKyNFXZX0g3rxPb1H80KSB+UdLbSQM+wF+AiZJmZp3j8w2hSk+Dyj624N2kQS8IgtHBElIFaTCpImmSpBskre3SZ7Pq2e2299reQwpOG9OhilWqZnYPA2dI+rGk02y/WCcgB4C7JM0AziRNXdvVptbTSVPatuTvTicF4GWtHwXW2t6Zz/lCPl7X/m+2t+fPW4Hj6/pRos5vg7cA/6rxUa4IAiyQtAN4gHRPmkzzvjWj7vpW9jlP0Tggqdk9JggqiQzwyHAFcKOkh4B9wFea2C0AVkj6Dunm0pjGsEzSZNIT8u+AHcCsQrs7gbWS5gHfzPOAVwM/JAXBVfyGPC2jFbZ3SOoHHgGeIk3JwPaBXFK8NmcB9pPmAf8BWJQH26va7GMdc4C72tEaBMHQIunbpKrMuaT3G9YB2H4K+FqzAFjSN4D5eXeu7WdLJu1WzxoUq2eVdrafyFnGucBVkjbY/kGNT0hTyL5KymauaGLTTOvNti8vHT++QmtV5ayufbE69iowjvap9Fui7Ypg1jSbNNafYnufpHvpripYd33r+vx64N8dnCcIwHZsPbAB5wCrWtjcDkweaa1t9GUj8OaR1hFbbL2+kTJ8A8D4vN8PTC/ZrO3S956KYx8CHgLeAByVzz2DlA0caMNuIjA223yG9J7B4LmACaTsZvGcY4DHSQ/7fR1onQI8Cbwt7x8LvLNC61TgCWBCw67D9gtJSZUq7XvKn5v5rdD/j8bfKu8/TcoMH3L+/N084M78+b2kYHR2Vd/qdHZwfRcCVxSu2WMj/b8Q25G3xRSIHkDStaQXD5a2MF1Eehlu1CLprcDVtnePtJYg6GWUfrJwOfA52y/nw9eQfkJxSLC9DbgJeBDYDCy33d+B3QeAB3M1ajGpKlZstwvYlF/AWpaPHSBVsW71wV9EaEfro8D3gA252vdbKsZX24+QXiC8L08huLqT9nXaX4suYAOHvpRcxz3A67K/paRpEJV9q9PZ7vUtMQe4u02dQTCI7Nf6zlIQBEEQHIqkCaTg52OkQKY8/emIIL/8to0U6D850nqGizzv+TLbXxppLXVIWgdcbvvxkdYSHFnEHOAgCILgsJMzfW39Hu5oRdIU4NekF7N6JvgFsN2vtBBSXyeZ7+Ek/1rF+gh+g26IDHAQBEEQBEHQU8Qc4CAIgiAIgqCniAA4CIIgCIIg6CkiAA6CIAiCIAh6igiAgyAIgiAIgp4iAuAgCIIgCIKgp4gAOAiCIAiCIOgpIgAOgiAIgiAIeooIgIMgCIIgCIKe4v94XiImbDJBSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "spec = fig.add_gridspec(2, 3)\n",
    "\n",
    "ax0 = fig.add_subplot(spec[:, 1:])\n",
    "plt.scatter(X1_cumul_probs,X2_cumul_probs,color='darkorange',edgecolor='black',s=10,label='P-P plot')\n",
    "plt.plot([0,1.0],[0,1.0],ls='--',color='red')\n",
    "plt.grid(); plt.xlim([0.0,1.0]); plt.ylim([0.0,1.0]); plt.xlabel(r'$X_1$ - Porosity Percentile (fraction)'); plt.ylabel(r'$X_2$ - Porosity Percentile (fraction)'); \n",
    "plt.title('P-P Plot'); plt.legend(loc='lower right')\n",
    "\n",
    "ax10 = fig.add_subplot(spec[0, 0])\n",
    "plt.hist(X1,bins=np.linspace(xmin,xmax,30),color='red',alpha=0.5,edgecolor='black',label=r'$X_1$',density=True)\n",
    "plt.hist(X2,bins=np.linspace(xmin,xmax,30),color='yellow',alpha=0.5,edgecolor='black',label=r'$X_2$',density=True)\n",
    "plt.grid(); plt.xlim([xmin,xmax]); plt.ylim([0,15]); plt.xlabel('Porosity (fraction)'); plt.ylabel('Density')\n",
    "plt.title('Histograms'); plt.legend(loc='upper right')\n",
    "\n",
    "ax11 = fig.add_subplot(spec[1, 0])\n",
    "plt.scatter(np.sort(X1),np.linspace(0,1,len(X1)),color='red',edgecolor='black',s=10,label=r'$X_1$')\n",
    "plt.scatter(np.sort(X2),np.linspace(0,1,len(X2)),color='yellow',edgecolor='black',s=10,label=r'$X_2$')\n",
    "plt.grid(); plt.xlim([xmin,xmax]); plt.ylim([0,1]); plt.xlabel('Porosity (fraction)'); plt.title('CDFs'); plt.legend(loc='lower right')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.4, wspace=0.3, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of QQ-plot and PP-plot in Python.\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at [Python Demos](https://github.com/GeostatsGuy/PythonNumericalDemos) and a Python package for data analytics and geostatistics at [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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